Method and system for characterising an optical lens

Abstract

The invention relates to a characterisation method (100) for characterising one or more optical lenses, the method comprising the following steps carried out for at least one optical lens (OO): - measuring (102) an optical transfer function, FTO, referred to as the test FTO, at a plurality of measurement points defined by a test plane; and - identifying (106), from among a plurality of clusters, a cluster, referred to as the target cluster, to which the lens belongs; - providing (110), for the optical lens (OO) and on the basis of the target cluster, a calibration profile for correcting an image captured with the optical lens. The invention also relates to a characterisation system for characterising an optical lens implementing such a characterisation method, and to a method and system for manufacturing an optical lens implementing such a characterisation method and system.

Description

DESCRIPTION Title: Method and system for characterizing an optical objective

[0001] The present invention relates to a method for characterizing optical lenses, and in particular for calibrating said optical lenses. It also relates to a system implementing such a method. Furthermore, it relates to a method and system for manufacturing optical lenses implementing such a characterization method and system.

[0002] The field of the invention is generally the field of characterization of an optical objective, in particular an optical objective used for imaging. State of the art

[0003] An optical lens, or simply lens, comprises several optical elements, such as lenses, spacers, etc., stacked in a barrel according to a specific stacking direction and assembly instructions. Once manufactured, the optical lens undergoes quality testing to determine whether its image quality is satisfactory. One technique used to test lens quality involves measuring a modulation transfer function (MTF) and comparing the measurement to a predetermined threshold. If the lens quality is unsatisfactory, it is simply discarded.

[0004] If the lens quality is satisfactory, then the optical lens is retained. Furthermore, the optical lens can, in some cases, be calibrated to correct any defects found, for example, based on the MTF (Maximum Transient Factor) measured during pre-production, such as during the lens's design. Generally, calibration involves providing a correction function, also called a calibration function, which is used to digitally correct an image acquired with the optical lens. In summary, the acquired image is digitally processed to apply the calibration / correction function, for example by convolution, and correct said image.

[0005] In the current solution, each lens is tested by measuring the MTF according to a test plan. To avoid slowing down the manufacturing rate, the test plan includes a small number of measurement points: the measurement performed according to the test plan does not provide highly accurate calibration data. Furthermore, the current solution is subject to MTF measurement uncertainties that can vary from one lens to another, impacting the quality of lens calibration.

[0006] One objective of the present invention is to remedy at least one of the aforementioned drawbacks.

[0007] Another aim of the invention is to provide a solution for characterizing an optical objective allowing for more efficient and precise calibration of optical objectives, without impacting the manufacturing rate of said optical objectives. Description of the invention

[0008] The invention proposes to achieve at least one of the aforementioned goals by means of a method for characterizing optical objective(s) comprising the following steps carried out for at least one optical objective: - measurement of an optical transfer function, OTF, called test OTF, at several measurement points defined by a test plan; - identification, among several clusters, of a cluster, called the target cluster, to which the said objective belongs; and - provision, for said optical lens and according to said target cluster, of a calibration profile to correct an image captured with said optical lens.

[0009] Thus, the invention proposes a more efficient optical objective characterization solution that allows for better characterization of said optical objective, compared to current solutions.

[0010] Indeed, like the current solution, the invention performs a measurement of a test FTO used for optical objective validation according to a test plan that may include a limited number of measurement points. Therefore, the invention does not reduce the manufacturing rate compared to current solutions.

[0011] Furthermore, the invention proposes assigning each optical objective to a cluster of optical objectives and calibrating said optical objective with a calibration profile associated with said cluster or a function of said cluster. This allows for differentiated calibration of the optical objectives since it is dependent on the cluster to which each optical objective is assigned, while avoiding drifts / errors due to FTO measurement uncertainties, which is more efficient.

[0012] Furthermore, using a calibration profile associated with the cluster allows for the separation of optical lens calibration from the measured test FTO used for lens validation. This potentially enables the use of a more precise calibration FTO for calibration—that is, a calibration FTO measured using a significantly larger number of measurement points than the test FTO, for example, as defined in a calibration plan—and / or other parameters instead of or in addition to the measured test FTO. Thus, it is possible to achieve a much more precise calibration than that obtained with the test FTO measured using a limited number of measurement points. This provides greater flexibility in optical lens calibration, in terms of calibration data, calibration method, and calibration accuracy.

[0013] By "assembly recipe," we mean a set of parameters determining how to select a lens from several molded lenses and assemble the various types t of different lenses for a given objective, where t ranges from 1 to T for T lens types: for example, T ranges from 5 to 8 to create an objective with 5, 6, 7, or 8 lenses, respectively. In particular, each lens type t can be molded in multiple copies simultaneously with each injection into several indexed cavities k, ranging from 1 to K, of the same mold that holds them: for example, K ranges from 8 to 32 in the case of 8 to 32 cavities. Other manufacturing techniques Simultaneous printing of lenses of the same type, other than injection, is also conceivable, which may lead to differences between them, such as printing on a common wafer, engraving of optical diffraction gratings, etc.

[0014] A first set of parameters is the index k of the cavity from which the lens comes in the mold, or on the wafer carrying the lenses made, associated with the index t of each type of lens, relative to the common assembly.

[0015] Generally, micro-centering defects can appear specifically for each lens type of each index k and be partially compensated by a rotation 0k, t specific to each lens type t in the assembly. Other parameters can also be considered, such as the bearing pressure on the multiple assembled lenses, etc. In particular, when K lenses are molded or manufactured for each of the T lens types in the stack, K different assembly recipes are generally determined with the aforementioned sets of parameters, resulting in K different lens clusters at the production output, more precisely containing micro-differences that make their transfer function not strictly identical.

[0016] Depending on the embodiment, the optical transfer function, OTF, can be the modulation transfer function, MTF (or MTF for "Modulation Transfer Function").

[0017] Depending on the embodiment, the optical transfer function, OTF, can be the point spread function, PSF (or PSF for "Point Spread Function").

[0018] In the case where the FTO is the MTF, the MTF of a point corresponds to the contrast for that point.

[0019] Following an example implementation, the MTF measurement for a given point can be performed as follows. A target pattern is placed at a certain distance, called the scene distance, chosen from the sensor and the lens. This distance can also be achieved using an optical element placed between the target and the lens, in order to change the distance The apparent distance of the target from the sensor is beyond the physical distance between the target and the lens. This apparent distance can even be set to infinity to obtain a scene distance of so-called infinity. The lens may be moved relative to the sensor to select the best sharpness of the target for this apparent scene distance. (The best sharpness can be obtained on a specific area of ​​the sensor, on a single color component, but not necessarily on all three color components simultaneously, nor the entire sensor at once...). This target includes an alternating pattern: - of patterns, described as dark, which do not allow light to pass through, and - Light patterns corresponding to the color of the radiation. Dark patterns are assumed to represent patterns without radiation on the sensor since they do not allow radiation to pass through. Light patterns are assumed to represent patterns of the same color as the radiation since they allow the radiation to pass through.

[0020] In this configuration, the MTF of a point (u,v) on the sensor is calculated as follows. For an area centered on the point (u,v) and comprising several alternating patterns, several contrast values ​​are calculated based on the values ​​received by the pixels in that area, each contrast value corresponding to an alternating pattern in that area. The MTF value for the point (u,v) is calculated based on these measured values, for example, by averaging them.

[0021] Optionally, for a measurement point, the MTF can be measured for several, for example 3 or 5, spatial pattern frequencies.

[0022] Optionally, for a measurement point, the MTF can be measured for several directions, for example 2 or three spatial directions.

[0023] Following a non-limiting example, for a measurement point, the MTF can be measured: - for three spatial directions, for example 0°, 45° and 90°; and - in each direction, for three spatial frequencies of patterns. In this particular, non-limiting example, the MTF for said point is represented by 9 contrast values. Furthermore, if the MTF is measured for each color—red, green, and blue—individually, then the MTF for said measurement point is represented by 27 contrast values.

[0024] In the case where the FTO is the PSF, the PSF of a point (x,y) of the scene corresponds to the function describing the spreading of this point on the image sensor.

[0025] Following an example implementation, the PSF measurement for a given point can be performed as follows. A target with patterns is placed in front of the sensor and lens, for example, at a plane corresponding to maximum focus, either directly or via a distance-adaptive lens. This target includes a plurality of points that allow light to pass through. The remainder of the target is opaque and does not allow light to pass through.

[0026] Each point located at a position (x,y) of the scene is captured on the sensor in the form of a spot of varying size and shape.

[0027] When the task is a perfect circle centered (u,v) on the sensor, then the value of the PSF for the point (x,y) corresponds to the radius of the task captured on the sensor and centered on the position (u,v) of the sensor.

[0028] When the task has a different shape, the PSF can, for example, be calculated as the mean radius of the task. The mean radius can be calculated as follows. First, the center of the task is extracted, for example, by determining the centroid of the positions of the points forming the task, weighted by the intensity detected at each position. Then, the sum of the detected amplitudes is calculated, multiplied by the square of the distance to the centroid. This sum is divided by the sum of the detected amplitudes. This gives the square of the mean radius of the task. The square root is taken. This gives the effective mean radius of the task.

[0029] Following a non-limiting example, for a measurement point, the PSF can be represented by a single value. Furthermore, if the PSF is measured for each color (red, green, blue) individually, then the PSF for that measurement point is represented by three values.

[0030] The FTO can be measured with white radiation, meaning that the light patterns are white. In this case, the FTO measured for a point includes a single value for all colors. In this case, the FTO measured at a point can be denoted FTO W , for example MTF W Or PSF W

[0031] Alternatively, the FTO can be measured individually for at least one monochrome color radiation, and in particular for red, and / or green, and / or blue radiation. In this case, the FTO measured at a point includes an individual FTO value for that color. In this case: - where applicable, the FTO measured at a point for the color red can be noted as FTO r , for example MTF r in the case where the FTO is the MTF or PSF r in the case where the FTO is the PSF; - where applicable, the FTO measured at a point for the color green can be noted as FTO 9 , for example MTF 9 in the case where the FTO is the MTF or PSF 9 in the case where the FTO is the PSF; and - where applicable, the FTO measured at a point for the color blue can be noted as FTO b , for example MTF b in the case where the FTO is the MTF or PSF bin the case where the FTO is the PSF.

[0032] For at least one, in particular each, measurement point, the FTO can be measured for a unique spatial frequency of patterns.

[0033] For at least one, and in particular each, measurement point, the FTO can be measured for several spatial frequencies of patterns. According to one embodiment, for at least one, and in particular each, measurement point, the FTO can be measured for 3, 4, or 5 spatial frequencies of patterns.

[0034] For at least one, in particular each, measurement point, the FTO can be measured for a single spatial direction of pattern repetition.

[0035] For at least one, and in particular each, measurement point, the FTO can be measured for several spatial directions of pattern repetition. Following an example embodiment, for at least one, and in particular each, measurement point, the FTO can be measured for three spatial directions of pattern repetition, for example, 0°, 45°, and 90° in a predetermined reference plane.

[0036] Thus, for a given optical objective, when the FTO is measured for N points (u,v) of said optical objective, with N>1, then the FTO can be represented by a vector, of: - N values ​​if a single FTO value is measured for each point, or - N sets of values, if several values ​​are measured for each measurement point, as described above.

[0037] The various options that have just been described for an FTO in the general sense can be applied to a test FTO.

[0038] The various options just described for an FTO in the general sense can be applied to a calibration FTO, as described later.

[0039] In the following, without loss of generalities, we consider that the test plan includes N measurement points so that the measured test FTO includes N values, or N sets of values.

[0040] The test plan includes a number of measurement points distributed across the plane of the optical lens or image sensor. For example, the test plan might include 5, 10, 20, or 50 measurement points.

[0041] Preferably, the test plan includes a small number of measurement points so as not to impact, or to have the least possible impact on, the production rate. Thus, the invention can be integrated into an optical lens manufacturing line without impacting, or with a negligible impact on, the production rate.

[0042] For at least one, in particular each, measurement point, the test plan may include the measurement of the FTO for white radiation, or individually for at least one monochrome radiation, and in particular for red radiation, and / or for green radiation and / or for blue radiation.

[0043] For at least one, in particular each, measurement point, the test plan may include the measurement of the FTO for a single spatial frequency of patterns.

[0044] For at least one, and in particular each, measurement point, the test plan may include FTO measurement for multiple spatial pattern frequencies. According to one example embodiment, for at least one, and in particular each, measurement point, the test plan may include FTO measurement for 3, 4, or 5 spatial pattern frequencies.

[0045] For at least one, in particular each, measurement point, the test plan may include the measurement of the FTO for a single spatial direction of pattern repetition.

[0046] For at least one, and in particular each, measurement point, the test plan may include the measurement of the FTO for several spatial directions of pattern repetition. Following an example embodiment, for at least one, and in particular each, measurement point, the test plan may include the measurement of the FTO for three spatial directions of pattern repetition, for example, 0°, 45°, and 90° in a predetermined reference plane.

[0047] According to embodiments, the method according to the invention may further include, after the measurement step of the test FTO, a validation step of said optical objective by comparison of said measured test FTO to at least a predetermined threshold.

[0048] When one or more thresholds are not met, the optical objective can be considered unsatisfactory and discarded. In this case, it is possible to stop the process according to the invention for that objective and not carry out the other steps of the process according to the invention.

[0049] When the threshold(s) are met, then the optical objective can be considered satisfactory and the rest of the process can be implemented.

[0050] At least one threshold can be the same for all measurement points. Alternatively, and preferably, at least one predetermined threshold can be different for at least two measurement points. For example, the FTO measured at a point located at the center of the optical lens can be compared to at least a first threshold, and the FTO measured at a point located on the periphery of the optical objective can be compared to at least a second threshold different from said at least a first threshold.

[0051] For a measurement point, at least one FTO threshold may be identical for at least two spatial pattern frequencies. Alternatively, or in addition, at least one predetermined threshold may be different for at least two spatial pattern repetition frequencies.

[0052] For a measurement point, at least one threshold may be identical for at least two spatial directions of pattern repetition. Alternatively, or in addition, at least one predetermined threshold may be different for at least two spatial directions of pattern repetition.

[0053] Depending on the embodiment, the identification of the target cluster can be carried out based on at least one of the following data: - a recipe for assembling optical elements to manufacture said optical lens; - an assembly line for said optical objective; - a set of optical lenses to which said optical lens belongs; - the date of manufacture, - etc.

[0054] For example, clusters can be defined based solely on the assembly recipe, or the assembly line or batch of the manufactured objective, the manufacturing date, etc. In this case, the identification of the target cluster for a target objective is carried out based on the assembly recipe used to manufacture that objective, or the assembly line used to manufacture that objective, the batch to which that objective belongs, the manufacturing date of that objective, etc.

[0055] In this case, the identification of the target cluster does not take into account, or at least not solely, the test FTO measured for said optical objective. Thus, in this embodiment, in certain cases, the cluster identification step can be performed before the test FTO measurement step.

[0056] Depending on the embodiment, the identification of the target cluster can be carried out based on the measured test FTO.

[0057] In this case, the test FTO can be compared to test FTOs measured for previously treated objectives in order to identify the target cluster.

[0058] Alternatively, the test FTO can be compared to a reference FTO associated with each cluster to identify the target cluster. In this case, the target cluster may be the cluster whose reference FTO is closest to the measured test FTO. Alternatively, the target cluster may be the cluster whose reference FTO: - is closest to the measured test FTO, and - presents a difference less than or equal to at least a difference threshold. The at least one deviation threshold may be identical for all measurement points, and / or all spatial frequencies of patterns, and / or all spatial directions of pattern repetition. Alternatively, the at least one deviation threshold may be different for at least two measurement points, and / or for at least two spatial frequencies of patterns, and / or for at least two spatial directions of pattern repetition.

[0059] Following implementation examples, for at least one cluster, the reference FTO can be calculated based on the test FTOs measured for the optical objectives belonging to said cluster.

[0060] For example, for at least one cluster, the reference FTO can be calculated as the average of the test FTOs measured for the optical objectives belonging to said cluster.

[0061] In this case, according to an optional feature, the method according to the invention may further include an update of the reference FTO of the target cluster with the measured test FTO.

[0062] In particular, when the reference FTO is calculated as the average of the test FTOs measured for the optical objectives belonging to said cluster, the reference FTO of the target cluster can be recalculated, or updated, by taking into account the test FTO measured for the optical objective that has just been assigned to said target cluster.

[0063] Thus, it is possible to evolve the reference FTO to follow the manufacturing deviation(s) that may occur over time.

[0064] In cases where the target cluster is identified based on the test FTO, said clusters can be built beforehand.

[0065] For example, clusters can be built during the manufacturing of optical lenses. For example, at the beginning of manufacturing, an initial batch of lenses can be used to build clusters based on the test FTOs measured for those lenses.

[0066] Alternatively, clusters can be built prior to the manufacturing of optical objectives. For example, clusters can be built based on test FTOs obtained through simulation before the manufacturing process.

[0067] Of course, clusters can be constructed using other techniques without departing from the scope of the present invention and the examples given above are by no means limiting.

[0068] The calibration profile associated with a cluster can include any type of data that can be used to correct an image acquired with the optical lens.

[0069] The calibration profile may include at least one aberration function representative of the aberrations introduced into the image by the optical lens: in this case, this at least one aberration function is inverted to cancel / neutralize some or all of said aberrations in said image.

[0070] Preferably, the calibration profile may include at least one function to correct aberrations introduced into the image by the optical lens. In this case, this at least one correction function is applied to the values ​​representing the captured image to correct, at least partially, said aberrations.

[0071] The correction function, or at least one correction function, or respectively the aberration function, can be represented by: - at least one dataset, and in particular by at least one data vector or at least one data matrix; or - at least one mathematical relationship.

[0072] Depending on the embodiment, for at least one cluster, the calibration profile associated with said cluster can be a function of a calibration FTO associated with said cluster.

[0073] The calibration profile may include said calibration FTO.

[0074] The calibration profile may include at least one correction function derived from said calibration FTO.

[0075] For at least one cluster, the calibration FTO can correspond to, or be determined from, the reference FTO of said cluster.

[0076] In this case, the calibration FTO includes the same number of measurement points as the reference FTO, and therefore the test FTO.

[0077] According to embodiments, for at least one cluster, the calibration FTO can be determined according to a calibration plan comprising more measurement points than the test plan.

[0078] In this case, the calibration FTO is more accurate than the test FTO and allows for more precise calibration, and therefore better correction of images acquired with the optical lens.

[0079] For example, the calibration FTO may include a number of measurement points greater than 20, and in particular 64 measurement points.

[0080] In the following, without loss of generalities, we consider that the calibration plan includes NN measurement points so that the calibration FTO includes NN values, or NN sets of values.

[0081] The calibration plan can include a number NN of measurement points distributed in the plane of the optical lens or image sensor. For example, the calibration plan can include a number NN of measurement points that is greater than or equal to 20, in particular 50, and even more particularly 60.

[0082] The calibration plan includes a high number of measurement points, in any case greater than the test plan.

[0083] For at least one, and in particular each, measurement point, the calibration plan may include the measurement of the FTO for white radiation, or individually for at least one monochrome radiation, and in particular for red radiation, and / or for green radiation and / or for blue radiation.

[0084] For at least one, in particular each, measurement point, the calibration plan may include the measurement of the FTO for a single spatial frequency of patterns.

[0085] For at least one, and in particular each, measurement point, the test plan may include measuring the FTO for several spatial pattern frequencies. According to an example embodiment, for at least one, and in particular each, measurement point, the calibration plan may include measuring the FTO for 3, 4, or 5 spatial pattern frequencies.

[0086] For at least one, in particular each, measurement point, the calibration plan may include the measurement of the FTO for a single spatial direction of pattern repetition.

[0087] For at least one, and in particular each, measurement point, the calibration plan may include the measurement of the FTO for several spatial directions of pattern repetition. According to one embodiment, for at least one, and in particular each, measurement point, the calibration plan may include the measurement of the FTO for three spatial directions of pattern repetition, for example, 0°, 45°, and 90° in a predetermined reference plane.

[0088] For at least one cluster, the calibration profile can be obtained by simulation using numerical models of optical lenses. In this case, the calibration profile can be obtained before the optical lenses are manufactured.

[0089] For at least one cluster, the calibration profile can be obtained by measuring optical objectives that are part of said cluster, during the manufacture of said objectives.

[0090] For example, at the beginning of the manufacturing process, an initial batch of lenses can be used to obtain the calibration profile. Specifically, the calibration profile can be measured on each lens in this first batch. part. Then the calibration profile associated with the cluster can be obtained by calculation from the measured calibration profiles, for example as an average of said measured calibration profiles.

[0091] When a cluster's calibration profile is a function of the calibration FTO associated with that cluster, a first set of objectives can be used to obtain the calibration FTO. Specifically, the FTO can be measured, according to the calibration plan, on each objective in this first set. Then, the calibration FTO associated with the cluster can be calculated from the FTOs measured according to the calibration plan, for example, as an average of the measured FTOs.

[0092] If the calibration profile includes another function, the same operation can be performed for that other function.

[0093] According to an advantageous embodiment, for at least one cluster, the calibration profile associated with said cluster can be updated every K objectives, and in particular every K characterized objectives or every K assigned objectives to said cluster, with K > 2 and in particular K > 100 and even more particularly K > 1000 or K > 10,000 or even K > 100,000.

[0094] For example, the calibration profile can be measured for an optical lens, or for all M optical lenses during manufacturing. Then, the calibration profile associated with the cluster to which that optical lens is assigned can be updated based on the calibration profile measured on that optical lens.

[0095] When the calibration profile is a function of the calibration FTO, the FTO of said objective can be measured according to the calibration plan for each objective every M objectives. Then, the calibration FTO of the cluster to which said objective is assigned can be updated based on said FTO measured according to the calibration plan for said optical objective.

[0096] As mentioned above, the calibration profile may include, or may be a function of, an FTO associated with the cluster.

[0097] Of course, the calibration profile can be a function of, or include, other data or function, in addition to or instead of an FTO.

[0098] In accordance with non-limiting embodiments, for at least one cluster, the calibration profile associated with said cluster may include, consist of, or be a function of, at least one of the following functions or parameters: - MTF on and off axis, i.e., MTF in and off the optical axis: indeed, the more inclined the angle of incidence is with respect to the optical axis of symmetry, the faster the MTF decreases with respect to the spatial frequency of the observed patterns. Therefore, we can plot this MTF curve versus the spatial frequency, the set of several curves as a function of the angle with the axis, or even a list of spatial frequencies at a given MTF value; - MTF at best lens performance, i.e. the MTF after compensation of any tip and tilt between a plane of the lens and the plane of the sensor; - Multi-frequency MTF measurement: Measurement of MTF at several spatial frequencies. MTF generally decreases as spatial frequency increases. We then want to know at what rate. But more generally, we have a curve, which can even rise locally at certain spatial frequencies; - Effective Focal Length (EFL): Effective focal length, as opposed to other focal lengths, which are taken for example at lens support points like the FFL. Effective in the sense that it is the "effective" parameter to be used in the conjugation formula, for a geometric optics approach, to calculate the relationship of the distances of the source and focal points, excluding infinite distance: 1 / dl + l / d2 = 1 / EFL; - Flange Focal Length (FFL): distance between the "flange" and the sensor, i.e. the mechanical support area of ​​the lens and the sensor, to obtain a sharp point when the image comes from infinity; - Field curvature: The curvature of the field, that is, the non-flatness of the surface where, for example, an image from infinity is formed. In other words, the sensor would need to respect this curved surface to obtain a sharp image when it comes from infinity. This is somewhat of a flaw because the sensors are flat except in exceptional cases; - Multi Frequency Focus Shift: Shifts the focus according to the spatial frequency of the observed pattern; - Tilt of image plane: the inclination of the image plane, therefore relative to the plane perpendicular to the theoretical axis of the lens, or to the axis perpendicular to the lens mounting plane. This is a defect that should not be too great; - Depth of Focus (DOF): the distance at which the sensor must be placed to obtain a sharp image. The mirror image of depth of field, but on the sensor side; - Best focus position: where the sensor should be placed to achieve the best focus; - Astigmatism; - Relative illumination; - Chromatic aberration;

[0099] According to another aspect of the present invention, a system for characterizing optical objective(s) is proposed, said system comprising: - at least one device for measuring an optical transfer function, OTF; and - a computing unit; configured to implement the steps of the process according to the invention.

[0100] The system includes, in terms of hardware and / or software, at least one, or any combination of at least two, of the characteristics described above with reference to the method according to the invention and which are not repeated here for the sake of brevity.

[0101] According to another aspect of the present invention, a method for manufacturing optical lenses is proposed comprising the following steps for at least one optical lens: - manufacturing of said optical lens, - characterization of said optical objective by the characterization method according to the invention.

[0102] According to another aspect of the present invention, an optical lens manufacturing installation is proposed comprising: - a manufacturing line for optical lenses, and - a characterization system according to the invention to characterize at least one optical objective.

[0103] According to another aspect of the present invention, an optical objective is proposed characterized by the characterization method, or system, according to the invention.

[0104] The optical objective includes, or is associated with, a calibration profile determined by the method or system according to the invention.

[0105] The calibration profile can be in the form of software, a computer program, or a set of data representing said calibration profile, and stored in a non-transient manner in a storage means.

[0106] According to another aspect of the invention, a device is proposed, and in particular a user device, comprising at least one optical objective according to the invention.

[0107] In particular, the device can be a user device such as a smartphone, tablet, etc.

[0108] The user device may also include a display screen.

[0109] In particular, the device can be a user device such as a computer.

[0110] The computer-type user device may also include a display screen. [YES] In particular, the device can be a television.

[0112] Television may also include a display screen.

[0113] In particular, the device can be: - a virtual reality headset or glasses; or - an augmented reality headset, or glasses.

[0114] The helmet, or glasses respectively, according to the invention may further comprise at least one display screen.

[0115] In particular, the device may be a medical imaging device.

[0116] In particular, the medical imaging device can be an endoscope, an ultrasound machine, etc.

[0117] The medical imaging device may also include at least one display screen.

[0118] Of course, the device according to the invention is not limited to the examples of devices that have just been given.

[0119] According to another aspect of the present invention, a vehicle is proposed comprising at least one optical objective according to the invention.

[0120] The vehicle may further include a display screen, for example arranged in a passenger compartment of the vehicle, or a projector to project at least one image onto a display surface generally known as a "head-up display".

[0121] Depending on the embodiment, the vehicle can be a land vehicle, such as a car, autonomous, semi-autonomous or non-autonomous.

[0122] Depending on the embodiment, the vehicle can be a flying vehicle, such as a drone, an airplane, a helicopter, autonomous, semi-autonomous or non-autonomous.

[0123] Depending on the embodiment, the vehicle can be a maritime vehicle, such as a boat or a submarine, autonomous, semi-autonomous or non-autonomous. Description of the figures and methods of implementation

[0124] Other advantages and features will become apparent upon examination of the detailed description of non-limiting embodiments and the accompanying drawings, in which: - FIGURES 1 and 2 are schematic representations of non-limiting examples of embodiments of a characterization process according to the invention; - FIGURE 3 is a schematic representation of a non-limiting example of an embodiment of an optical lens characterization system according to the invention; - FIGURE 4 is a schematic representation of a non-limiting example of an embodiment of a method for manufacturing optical lens(es) according to the invention; - FIGURE 5 is a schematic representation of a non-limiting example of an installation for manufacturing optical lens(es) according to the invention; - FIGURE 6 is a schematic representation of a non-limiting example embodiment of a camera module according to the invention; - FIGURE 7 is a schematic representation of a non-limiting example of an image acquisition method according to the invention; - Figures 8a-8c are schematic representations of non-limiting examples of embodiments of a device according to the invention; and - FIGURE 9 is a schematic representation of a non-limiting example embodiment of a vehicle according to the invention.

[0125] It is understood that the embodiments described below are by no means exhaustive. In particular, variants of the invention may be conceived that include only a selection of features. The following features are described in isolation from the other described features, if this selection of features is sufficient to confer a technical advantage or to differentiate the invention from the prior art. This selection includes at least one preferably functional feature without structural details, or with only a portion of the structural details if that portion alone is sufficient to confer a technical advantage or to differentiate the invention from the prior art.

[0126] In particular, all the variants and embodiments described can be combined with each other if there are no technical obstacles to this combination.

[0127] In the figures and in the rest of the description, elements common to several figures retain the same reference.

[0128] FIGURE 1 is a schematic representation of a non-limiting example embodiment of a method for characterizing an optical objective according to the present invention.

[0129] The method 100 of FIGURE 1 can be used to characterize an optical lens 00 used for imaging, that is, for acquiring an image or video. Such a lens 00 can be combined with an image sensor to form an imaging module, or a camera module, and integrated into various devices or vehicles.

[0130] An optical lens is generally composed of several optical elements, such as lenses, spacers, etc. The different elements of the optical lens are stacked according to a stacking direction, which also corresponds to the axis of the optical lens.

[0131] The optical lens 00 can be associated with an image sensor (not shown in FIGURE 1). In this case, this image sensor is used for the characterization of said optical lens 00.

[0132] Alternatively, the optical lens may not be associated with an image sensor. In this case, an image sensor from a measuring device is used for the characterization of said optical lens 00, for example within a camera module.

[0133] The process 100 includes a step 102 of measuring a test FTO, denoted FTOt hereafter, according to a test plan.

[0134] The test plan includes a limited number N of FTO measurement points for the optical objective. The measured FTO can be the PSF or the MTF. For example, N = 10, 20, or 50 points.

[0135] For each measurement point, the FTO is measured by placing a target comprising a series of patterns at that measurement point. The target is illuminated by a light source. The light from the target passes through the optical lens and is captured by an image sensor, such as a CMOS or CCD sensor. Then, the image of the pattern series obtained on the image sensor is analyzed to determine the FTO.

[0136] The FTO can be measured for white light. Alternatively, the FTO can be measured for at least one, and in particular several, of the following monochrome radiations: red, green, and blue.

[0137] For each measurement point, the FTO can be measured for one or more spatial directions of pattern repetition. For example, for each measurement point, the FTO can be measured for three spatial directions: 0°, 45°, and 90°.

[0138] For each measurement point, the FTO can be measured for one or more spatial pattern frequencies. For example, for each measurement point, the FTO can be measured for three spatial frequencies: 0°, 45°, and 90°.

[0139] Thus, following an example embodiment, the FTOt comprises a FTO measured at 10 measurement points, and for each measurement point, in three spatial directions, and for each spatial direction, for three spatial frequencies of pattern repetition, and this for each color red, green, and blue individually. Therefore, the FTOt comprises, for each color, 10 measurement points and for each measurement point, 9 values.

[0140] Step 102 therefore provides an FTOt represented by three vectors: - a vector for each color: red, green, and blue; and - each vector comprises 10 sets of values; and - each set of values ​​comprising 9 values.

[0141] In an optional step 104, the test FTOt is used to test the quality of the optical objective to determine whether it is retained or not, and optionally, to classify it into one of several quality categories. To do this, the FTOt is compared to one or more threshold values. For example, the comparison can be performed individually for each measurement point, and / or for each spatial frequency, and / or for each spatial direction.

[0142] The threshold value(s) may be the same, or different: - for at least two measurement points, and / or - for at least two spatial repetition frequencies of patterns, and / or - for at least two directions of spatial repetition of patterns.

[0143] When the comparison indicates that the quality of the optical objective is not satisfactory, then it is discarded and process 100 is terminated. Otherwise, the optical objective is kept and process 100 continues.

[0144] During step 106, the test FTOt is used to identify a cluster, called the target cluster, denoted CC, to which said optical objective belongs, among several previously constructed Ci-Cm clusters.

[0145] To this end, following a non-limiting example, the FTOt measured in step 102 for objective 00 is compared to a reference FTO associated with each Ci-Cm cluster, respectively denoted FTORi-FTORm. Each reference FTO FTORi can be determined according to the test plan.

[0146] The target cluster CC can be the cluster G whose reference FTO FTORi is closest to the test FTO FTOt measured in step 102. The comparison of FTOt to each of the FTORi-FTORm values ​​can be performed in different ways. For example, the comparison can be performed individually for each measurement point, and / or for each spatial frequency, and / or for each spatial direction.

[0147] Step 106 identifies and indicates, among all the Ci-Cm clusters, the target CC cluster to which the optical objective belongs as a function of the FTOt measured in step 102.

[0148] For at least one, and in particular each, cluster G, the FTORi can be determined in different ways.

[0149] Following an example of implementation, the FTORi of cluster G can be determined as a function of the FTOt measured for each of the optical objectives belonging to said cluster.

[0150] In particular, the FTORi of cluster G can be determined as the average of the FTOt measured for each of the optical objectives that are part of said cluster G. The average can be calculated individually for each measurement point, and / or for each spatial frequency where applicable, and / or for each spatial direction.

[0151] In an optional step 108, the reference FTOR of the target cluster CC can be updated with the FTOt measured for the optical objective 00.

[0152] For example, when the FTOR of the target cluster CC corresponds to an average of the FTOts of the optical lenses belonging to said cluster CC, then the FTOR of said target cluster CC can be updated by taking into account the FTOt of optical lens 00 in said average

[0153] Of course, the update can be carried out using a different relationship.

[0154] The update can be performed individually for each measurement point, and / or for each spatial frequency and / or for each spatial direction.

[0155] In step 110, the calibration profile of the target cluster CC is obtained and associated with the optical lens 00. This calibration profile is used as the PC calibration profile of the optical lens 00 to correct images taken with said optical lens. In other words, the optical lens 00 benefits from a PC calibration profile associated with the target cluster CC to which it is assigned, and not a calibration profile that is specifically and individually measured for said optical lens 00, independent of other optical lenses.

[0156] The calibration profile may include any parameter, or any function that can be used to correct aberrations introduced by the optical lens 00 in an image taken with said optical lens.

[0157] Following an example implementation, the calibration profile can be, or may include, the reference FTOR associated with the target cluster CC, possibly after updating in optional step 108. In this case, the optical objective benefits from a calibration profile that depends on all the optical objectives within the cluster. Such a calibration profile reduces sensitivity to measurement deviations or uncertainties.

[0158] Following another embodiment, each Ci-Cm cluster can be associated with a calibration FTO, denoted FTOCi-FTOm, determined according to a calibration plan comprising a greater number of points than the test plan. In this case, the calibration profile can be, or include, the FTOC associated with the target cluster CC. Such a calibration FTO, FTOG, associated with a cluster G can be obtained from several FTOs, denoted FTOc, measured according to the calibration plan, for several optical objectives belonging to said cluster. For example, for each cluster G, the FTOG can be an average of the FTOc measured for several optical objectives belonging to said cluster G. Of course, the FTOG can be determined in a way other than that described here.

[0159] Alternatively, the FTOc of at least one optical lens can be determined by calculation during the design phase of said lens, for example using a modeling tool such as ZEMAX®. The "theoretical" FTOcs of the optical lenses belonging to a cluster G can then be used to calculate the FTOG of said cluster G.

[0160] Alternatively, the FTOc of at least one optical lens can be estimated by calculation using assembly and / or production parameters of that lens. In this case, these parameters are entered into an artificial intelligence model or a wave propagation model such as ZEMAX® to estimate the FTOc of the optical lens. The estimated FTOcs of the optical lenses belonging to a G cluster can then be used to calculate the FTOG of that G cluster.

[0161] The FTOCi of a cluster G can be determined using yet other techniques.

[0162] The calibration plan may include a number NN of FTO measurement points with the optical objective, with NN>N. For example, NN = 50 points or NN = 500 measurement points.

[0163] For each measurement point, the FTO can be measured / determined for: - for white light, or individually for one or more of the monochromatic red, green and blue radiations; - one or more spatial frequencies of pattern repetition; and / or - one or more spatial directions of pattern repetition; similarly to what is described above for FTOt.

[0164] Of course, the calibration profile can include other parameters, in addition to or instead of the FTOC calibration FTO. A non-exhaustive list of functions and / or parameters that the calibration profile can include is given above.

[0165] For at least one cluster, the calibration profile can be built during an initialization of process 100, carried out before step 102.

[0166] During an optional step 112, the PC calibration profile of the target cluster can be updated. For example, when the PC calibration profile matches, or includes, the FTOC of the target cluster, then step 112 can include: - a measurement / estimate, for optical objective 00, of a FTO, denoted FTOc, according to the calibration plan; and - an update of the FTOC of the cluster based on said FTOc measured for optical objective 00 according to the calibration plan.

[0167] For example, the FTOC can be updated by averaging the FTOc measured for optical objectives that are part of said target cluster CC, including the FTOc of optical objective 00 measured in step 112.

[0168] Of course, the calibration profile update can be done in another way.

[0169] The FTOC update can be performed individually for each measurement point, and / or for each spatial frequency and / or for each spatial direction that is part of the calibration plane.

[0170] For each cluster G, the optional update step 112 is not performed for each optical objective assigned to that cluster. The optional step 112 can be performed for every K objectives assigned to that cluster G during the manufacturing of optical objectives, with K > 1, for example, K = 500.

[0171] The Ci-Cm clusters used in process 100 can be constructed in different ways.

[0172] In the non-limiting example of FIGURE 1, process 100 may include an optional phase 120 for constructing said Ci-Cm clusters.

[0173] This phase 120 can, for example, be carried out with a first series of optical lenses, for example, a number L of optical lenses, during the manufacturing of optical lenses, and then perform steps 102 and following on a second series of optical lenses during manufacturing. The first and second series of optical lenses can be part of the same batch of optical lenses, a set of optical lenses manufactured by the same production line, a set of optical lenses manufactured on the same manufacturing date, a set of optical lenses having the same design, a set of optical lenses having the same assembly recipe, etc.

[0174] For example, L=1000 optical objectives.

[0175] During phase 120, the test FTO (FTOt) is measured for each of the L optical objectives in the first series. This yields L FTOt. These L FTOt are then organized into M Ci-Cm clusters using a given clustering technique. Various clustering techniques can be employed, such as a hierarchical dendrogram-type clustering technique, a k-means centroid method, or even a neural network previously trained for clustering.

[0176] Thus, process 100 constructs the Ci-Cm clusters used in step 106 starting from the FTOt of optical objectives from a first series of L optical objectives.

[0177] During this phase, for each cluster G, the FTORi of said cluster is calculated as a function of the FTOt measured for each optical objective, by any technique, for example by averaging as described above.

[0178] Optionally, the method according to the invention may include a phase 122 of constructing the calibration profile of at least one, and in particular of each, cluster G.

[0179] This phase 122 can be carried out after, or at the same time as, phase 120 of building Ci-Cm clusters.

[0180] When the PG calibration profile of cluster G includes, or matches, the reference FTO FTORi then the FTORi calculated in step 120 is stored in, or as, the calibration profile.

[0181] When the PG calibration profile of cluster G includes, or corresponds to, a calibration FTO FTOG determined according to a calibration profile comprising NN measurement points, then during step 122: - the FTOc is determined for each optical objective belonging to said cluster G; and - the FTOG is calculated for said cluster G, as a function of said FTOc, by any known technique, for example by averaging as described above.

[0182] For at least one optical lens, the FTOc of said optical lens can be measured with a measuring device. Alternatively, the FTOc of said optical lens can be calculated during the design phase of said optical lens using a modeling tool, for example, such as ZEMAX®. As yet another alternative, the FTOc of said optical lens can be estimated from assembly parameter(s) and / or manufacturing parameter(s) of said optical lens, using a modeling tool, for example, such as ZEMAX®, or a prediction tool, for example, a previously trained AI model.

[0183] When the PG calibration profile of cluster G includes, or corresponds to, other parameter(s) / function(s), these parameter(s) / function(s) can be measured / calculated during phase 122.

[0184] It should be noted that FTOt and FTOc can be the same function, for example, MTF or PSF. Alternatively, FTOt and FTOc can be different functions. For example: - the FTOt can be the MTF, respectively the PSF; and - FTOc can be PSF, respectively MTF.

[0185] Thus, process 100 provides a PC calibration profile associated with optical objective 00, this calibration profile being determined according to the cluster to which it belongs, and not solely from the characteristics of said objective 00 considered individually.

[0186] Furthermore, process 100 allows for a much more detailed calibration profile than that which could have been obtained from the limited FTOt of said optical objective 00 used to validate or not the quality of said objective in production.

[0187] FIGURE 2 is a schematic representation of another non-limiting example of a method for characterizing an optical objective according to the present invention.

[0188] The method 200 of FIGURE 2 can be used to characterize an optical lens 00 used for imaging, i.e., for acquiring an image or video. Such a lens 00 can be combined with an image sensor to form a camera module and integrated into various devices or vehicles.

[0189] Process 200 includes all the steps of process 100, except for the differences indicated below.

[0190] In process 200, the Ci-Cm clusters are not constructed from the FTOt of the optical lenses. In process 200, each cluster corresponds to an optical lens assembly recipe. Thus, process 200 does not include a cluster construction phase 120 since each cluster is defined by an assembly recipe.

[0191] According to a general formulation, each cluster G can correspond to at least one, or any combination of at least two, of the following parameters: - a recipe for assembling optical elements of optical lenses; and / or - an optical lens assembly line; and / or - a set of optical lenses; - a manufacturing date.

[0192] Thus, in process 200, there is no construction of the Ci-Cm: the latter are predefined.

[0193] Furthermore, in process 200, the identification of the cluster to which optical objective 00 belongs is carried out according to the assembly recipe of said objective, and more generally of the parameter(s) listed above, without use of the FTOt of said optical objective 00.

[0194] FIGURE 3 is a schematic representation of a non-limiting example embodiment of a system for characterizing an optical objective according to the present invention.

[0195] The system 300 of FIGURE 3 can be used to implement a process according to the invention, and in particular any one of the processes 100 or 200.

[0196] The 300 system includes a measuring device 302 for measuring an FTO with white light radiation, or individually with at least one of the following monochrome color radiations: red, green, and blue. The measuring device 302 may include at least one light source illuminating a patterned target.

[0197] It is worth noting that the FTO can be the MTF or the PSF.

[0198] The measuring device 302 can be configured to perform an FTO measurement according to a test plan comprising N measurement points on the optical objective. Optionally, the measuring device 302 can be configured to perform an FTO measurement according to a calibration plan comprising NN measurement points on the optical objective 00.

[0199] The 300 system also includes a 310 computing unit.

[0200] The calculation unit 310 includes an optional module 314. This optional module 314 can be configured, or programmed, to perform a comparison of an FTOt to at least one threshold value to determine whether an optical objective 00 is of satisfactory quality. Specifically, this optional module 314 can be configured to perform step 104 of processes 100 and 200.

[0201] The computing unit 310 includes a module 316. Module 316 can be configured, or programmed, to identify a target cluster (CC) to which an optical objective belongs, either based on the optical objective's FTOt or based on at least one other parameter related to the optical objective, such as the assembly recipe, design, assembly line, manufacturing date, etc. This module 316 can, in particular, be configured to perform step 106 of processes 100 and 200.

[0202] The computing unit 310 includes an optional module 318. Module 318 can be configured, or programmed, to update the FTOR of the target cluster CC. Specifically, this module 318 can be configured to perform step 108 of processes 100 and 200.

[0203] The 310 computing unit includes a 320 module. The 320 module can be configured, or programmed, to identify and associate a calibration profile with an optical objective. Specifically, this 320 module can be configured to perform step 110 of processes 100 and 200.

[0204] The 310 computing unit includes an optional 322 module. The 322 module can be configured, or programmed, to update the calibration profile of the target cluster CC. Specifically, this 322 module can be configured to perform step 112 of processes 100 and 200.

[0205] The computing unit 310 can also include an optional module 324. The module 324 can be configured, or programmed, to construct clusters from FTOt values ​​determined on a first series of optical objectives, either by calculation or by the measuring device 302. This module 324 can, in particular, be configured to carry out phase 120 of process 100.

[0206] The 310 computing unit can also include an optional 326 module. The 326 module can be configured, or programmed, to build a calibration profile for each cluster from parameters and / or functions determined on a first series of optical objectives, by calculation or by the measuring device 302. This module 326 can in particular be configured to carry out phase 122 of processes 100 or 200.

[0207] The computing unit 310 can be a hardware unit such as a processor, an electronic chip, a computer, a server, etc.

[0208] The computing unit 310 can be a software unit such as an application or a computer program.

[0209] The computing unit 310 can be any combination of at least one hardware unit and at least one software unit.

[0210] At least one of the 312-326 modules can be a hardware unit such as a processor, an electronic chip, a computer, a server, etc.

[0211] At least one of the modules 312-326 may be a software unit such as an application or computer program.

[0212] At least one of the modules 312-326 can be any combination of at least one hardware unit and at least one software unit.

[0213] At least one of the modules 312-326 can be independent of the modules 312-326.

[0214] At least two of the 312-326 modules can be integrated into the same module.

[0215] FIGURE 4 is a schematic representation of a non-limiting example embodiment of a method for manufacturing an optical lens according to the present invention.

[0216] The 400 process in FIGURE 4 can be used to manufacture optical lenses used for imaging, i.e., for acquiring an image or video.

[0217] The process 400 includes a step 402 of manufacturing an optical objective 00. The manufacturing of the optical objective 00 is carried out in a conventional and known manner and will therefore not be described in detail here.

[0218] The process 400 includes a step 404 of characterizing one, or each, optical objective manufactured in step 402, by the process according to the invention and in particular by method 100 or by method 200, to determine the PC calibration profile associated with said optical objective 00.

[0219] FIGURE 5 is a schematic representation of a non-limiting example embodiment of an installation for manufacturing an optical lens according to the present invention.

[0220] The manufacturing facility 500 in FIGURE 5 can be used to manufacture optical lenses used for imaging, i.e., for acquiring an image or video.

[0221] Installation 500 includes a manufacturing line 502 for optical lens(es). Manufacturing line 502 is a conventional optical lens manufacturing line and will therefore not be described in detail here.

[0222] Installation 500 further includes a system 504, which may be system 300 of FIGURE 3, to characterize one, or each, optical objective manufactured by manufacturing line 502, thus providing a calibration profile for at least one, and in particular, each optical objective.

[0223] FIGURE 6 is a schematic representation of a non-limiting example embodiment of a camera module according to the invention.

[0224] The camera module 600 of FIGURE 6 includes an optical lens 602 characterized according to the present invention, and in particular by the method 100 of FIGURE 1 or by the method 200 of FIGURE 2. The optical lens 602 can be the optical lens 00 of FIGURES 1-5.

[0225] In the non-limiting example shown in FIGURE 6, the optical objective 602 comprises four lenses 604-610, stacked in a barrel 612 along a stacking direction 614, which also corresponds to the axis 614 of the optical objective 602. Of course, the invention is not limited to an optical objective comprising four lenses.

[0226] The 600 camera module also includes a 616 image sensor associated with the 602 optical lens. The 616 image sensor can be any type of image sensor such as a CCD or CMOS sensor.

[0227] Of course, the 600 camera module may include other organs / components which are not shown in FIGURE 4, such as for example a device modifying the image field angle or a device modifying the depth of field of the imaged area.

[0228] The camera module 600 further includes a digital processing module 618 for the data captured by the image sensor 616. Such a digital processing module 618 can, for example, be arranged to perform a correction of the image captured by the image sensor 616, in order, for example, to correct at least part of the optical aberrations due to the optical lens 602. This module 618 can, for example, integrate the PC calibration profile determined by the method / system according to the invention.

[0229] The 618 processing module can be a standalone module. Alternatively, the 618 processing module can be integrated into another module or application, such as a photo application used for image acquisition.

[0230] FIGURE 7 is a schematic representation of a non-limiting example embodiment of an image acquisition method according to the invention.

[0231] The method 700 includes a step 702 of acquiring an image with an optical lens characterized by the present invention, or with a camera module according to the invention, and in particular the camera module 600 of FIGURE 6, or the lens 00 of FIGURES 1-5.

[0232] The method 700 further includes a step 704 for digitally processing the acquired image. Such digital processing can correct the acquired image in order, for example, to correct, at least partially, lateral chromatic aberration and / or geometric aberration due to the optical lens used for image acquisition. Such aberration correction can, for example, be performed using the PC calibration profile determined by the method according to the invention.

[0233] FIGURE 8a is a schematic representation of a non-limiting example embodiment of a device according to the present invention.

[0234] The apparatus 810 of FIGURE 8a comprises at least one camera module according to the present invention, and in particular the camera module 600 of FIGURE 6.

[0235] In the example shown in FIGURE 8a, device 810 is a smartphone, or a tablet.

[0236] Optionally, the device 810 may also include a display screen 812 equipped with a touch surface 814, for example capacitive.

[0237] The device 810 may further include an imaging application 816, such as a photo and / or video application, installed in said device 810 and executed by said device 810. The application 816 may, for example, integrate the processing module 618 of the camera module 600. Alternatively, the processing module 618 may be independent of the application 816.

[0238] FIGURE 8b is a schematic representation of another non-limiting embodiment of a device according to the present invention.

[0239] The device 820 of FIGURE 8b comprises at least one camera module according to the present invention, and in particular the camera module 600 of FIGURE 6.

[0240] In the example shown in FIGURE 8b, device 820 is a virtual reality, VR, or augmented reality, VA headset.

[0241] Optionally, the 820 headset may further include a display screen 822. Optionally, the 820 headset may further include a sensor (not shown) to detect the position aimed by one eye, or eyes, of the user on said display screen 822.

[0242] The 820 headset may further include an imaging application 824, such as a photo and / or video application, installed in and executed by the 820 headset. The imaging application may, for example, integrate the processing module 618 of the camera module 600. Alternatively, the processing module 618 may be independent of the 820 headset's imaging application.

[0243] FIGURE 8c is a schematic representation of another non-limiting embodiment of an apparatus according to the present invention.

[0244] The apparatus 830 of FIGURE 8c comprises at least one camera module according to the present invention, and in particular the camera module 600 of FIGURE 6.

[0245] In the example shown in FIGURE 8c, device 830 is a medical imaging device, such as an endoscope, an ultrasound machine, etc.

[0246] Optionally, the 830 medical imaging device can also include an 832 display screen.

[0247] Optionally, the 830 medical imaging device can also be equipped with a 834 sensing surface, for example capacitive.

[0248] Optionally, the 830 medical imaging device may further include a distal 836 light-gathering objective, which may or may not be part of the 600 camera module.

[0249] The medical imaging device 830 may further include an imaging application (not shown), such as a photo and / or video application, installed on and run by said device 830. The imaging application may, for example, integrate the processing module 618 of the camera module. Alternatively, the processing module 618 may be independent of the imaging application of the device 830.

[0250] FIGURE 9 is a schematic representation of a non-limiting example embodiment of a vehicle according to the present invention.

[0251] The vehicle 900 of FIGURE 9 includes at least one camera module according to the present invention, and in particular the camera module 600 of FIGURE 6, for example integrated into a camera 902.

[0252] In vehicle 900, the 902 camera module can, for example, be positioned behind the windshield, either at the top of the windshield or above it. Naturally, this positioning is given as a non-limiting example and the 902 camera module can be positioned elsewhere.

[0253] In the example shown in FIGURE 9, vehicle 900 is a land vehicle, in particular a car. Vehicle 900 may further include a display screen 904 equipped with a touch surface 906, for example capacitive, arranged in the passenger compartment of vehicle 900.

[0254] The 900 vehicle may also include an imaging application 908, such as a photo and / or video application. The imaging application 908 may, for example, integrate the processing module 618 of the camera module 600. Alternatively, the processing module 618 may be independent of the imaging application 908.

[0255] Of course, the invention is not limited to a land vehicle. A vehicle according to the invention can be a flying vehicle, such as a flying drone, an airplane, a helicopter, etc. A vehicle according to the invention can be a maritime vehicle, such as a marine drone, a boat, a submarine, etc.

[0256] The invention is not limited to the examples of devices and vehicles given with reference to the FIGURES. It can be implemented in any type of device and vehicle comprising a camera module.

[0257] Of course, the invention is not limited to the examples that have just been described.

Claims

DEMANDS 1. Method (100;200) for characterizing optical objective(s) comprising the following steps carried out for at least one optical objective (00): - measurement (102) of an optical transfer function, OTF, called the test OTF, at several measurement points defined by a test plan, and - identification (106), among several clusters, of a cluster, called the target cluster, to which said objective (00) belongs; - provision (110), for said optical lens (00) and according to said target cluster, of a calibration profile to correct an image captured with said optical lens (00).

2. Method (100;200) according to the preceding claim, characterized in that it further comprises, after the step of measuring the test FTO, a step (104) of validating or not said optical objective (00) by comparing said measured test FTO to at least a predetermined threshold.

3. A method (200) according to any one of the preceding claims, characterized in that the identification (106) of the target cluster is carried out based on at least one of the following data: - a recipe for assembling optical elements to manufacture said optical objective (00); - an assembly line for said optical lens (00); - a batch of optical lenses to which said optical lens (00) belongs; - etc.

4. Method (100) according to any one of the preceding claims, characterized in that the identification (106) of the target cluster is carried out as a function of the measured test FTO, and in particular by comparison of the measured FTO to a reference FTO associated with each cluster.

5. Method ((100;200) according to any one of the preceding claims, characterized in that it comprises a step (108) updating the reference FTO of the target cluster with the measured test FTO.

6. Method (100;200) according to any one of the preceding claims, characterized in that, for at least one cluster, the calibration profile associated with said cluster is a function of a calibration FTO associated with said cluster.

7. Method (100;200) according to claim 6 and any one of claims 4 or 5, characterized in that, for at least one cluster, the calibration FTO corresponds to, or is determined from, the reference FTO of said cluster.

8. Method (100;200) according to claim 6, characterized in that, for at least one cluster, the calibration FTO is determined according to a calibration plan comprising more measurement points than the test plan.

9. Method (100;200) according to any one of the preceding claims, characterized in that, for at least one cluster, the calibration profile associated with said cluster is updated (112) every K objectives, and in particular every K objectives characterized or every K objectives assigned to said cluster, with K>2.

10. Method (100;200) according to any one of the preceding claims, characterized in that the optical transfer function is the modulation transfer function, MTF (for "Modulation Transfer Function").

11. Method (100;200) according to any one of claims 1 to 9, characterized in that the optical transfer function is the point spread function, PSF (PSF for "Point Spread Function").

12. Method (100;200) according to any one of the preceding claims, characterized in that the measurement step (102) performs the measurement of the test FTO for white radiation.

13. Method (100;200) according to any one of the preceding claims, characterized in that the measurement step (102) performs the measurement of the test FTO individually for at least one monochrome color radiation, and in particular for red radiation, and / or green radiation and / or blue radiation.

14. Optical lens characterization system (300), said system (300) comprising: - at least one measuring device (302) for an optical transfer function, OTF; and - a computing unit (310); configured to implement the steps of the process (100;200) according to any one of the preceding claims.

15. A method (400) for manufacturing optical lenses comprising the following steps for at least one optical lens (00): - manufacturing (402) of said optical lens (00), and - characterization (404) of said optical objective (00) by the method (100;200) according to any one of claims 1 to 13.

16. Optical lens manufacturing installation (500) comprising: - a (502) optical lens manufacturing line, and - a characterization system (300;504) according to claim 14 for characterizing at least one optical objective (00).

17. Optical objective (00) characterized by the method (100;200) according to any one of claims 1 to 13.

Images