Method for acquiring a stack of images of a scene

Abstract

The invention relates to a method (100) for acquiring a stack of images (PIL) of a scene by means of at least one camera module of an apparatus, said method comprising the following steps: - acquiring (110) a plurality of images of the scene, at least two of the images being acquired with different focuses, and - storing (116) a stack of images comprising at least a portion of the acquired images; characterized in that the method additionally comprises, prior to the acquisition step (110), a step (102) of adjusting the number of images to be acquired according to at least one parameter relating to the apparatus and / or at least one parameter relating to the scene. The invention also relates to a computer program, a device, an apparatus and a vehicle implementing such a method.

Description

DESCRIPTION Title: Method for acquiring a stack of images of a scene.

[0001] The present invention relates to a method for acquiring a stack of images of a scene. It also relates to a computer program and a device implementing such a method. It further relates to a user device, a medical device and a vehicle implementing such a method.

[0002] The field of the invention is the field of acquiring image(s) of a scene with at least one camera module comprising an optical lens associated with a sensor. State of the art

[0003] Techniques are known for acquiring multiple images, forming a stack of images, of a scene at different focusing distances. For example, a technique called focus bracketing, or focus stacking, is known, which allows capturing a stack of images of a scene, each image being captured at a different depth of field. This stack of images is then used to represent the scene on a display medium, for example with a greater depth of field or with greater sharpness.

[0004] However, current techniques have significant drawbacks. On the one hand, current techniques are very resource-intensive in acquiring the image stack. On the other hand, the image stacks obtained with current techniques can be heavy in terms of memory space, especially if the definition of each image in the image stack is large. Finally, the acquisition of the image stack applies identically to all scenes, regardless of the device used.

[0005] An aim of the present invention is to remedy at least one of the drawbacks of the state of the art.

[0006] Another aim of the invention is to propose a more efficient solution for acquiring a stack of images of a scene.

[0007] Another aim of the invention is to propose a solution for acquiring a stack of images of a scene that is more suited to the conditions in which the scene is imaged. Statement of the invention

[0008] The invention proposes to achieve at least one of the aforementioned aims by a method of acquiring a stack of images of a scene with at least one camera module of a device, said method comprising the following steps: - acquisition of several images of said scene, at least two of said images being acquired at different focuses, and - storing a stack of images comprising at least a portion of said acquired images; characterized in that it further comprises, prior to the acquisition step, a step of adjusting the number of images to be acquired as a function of at least one parameter relating to said device and / or at least one parameter relating to said scene.

[0009] The invention proposes to obtain a stack of images of a scene, with a camera module, the focus of which is changed between each image. Thus, the stack of images comprises images of the scene comprising different areas of sharpness. Such an acquisition can for example be carried out by focus bracketing techniques, or any other technique known from the state of the art.

[0010] Unlike current techniques, the invention proposes to adjust, before the acquisition step, the number of images acquired during the acquisition step, and this as a function of one or more parameters of the scene and / or one or more parameters of the device, in particular at the time of acquisition of the image stack. The invention therefore makes it possible to adjust the number of images in the image stack as a function of the scene and / or the device carrying out the acquisition step. Thus, the invention makes it possible to optimize the acquisition of a stack of images of a scene, since said acquisition takes into account parameters of the scene, and / or parameters of the device carrying out the acquisition. In other words, the invention proposes to personalize the acquisition of a stack of images of a scene with an imaging device, to said scene and / or said device.

[0011] For example, when the scene is not very complex, the number of images to be acquired can be reduced without degrading the quality with which the scene is imaged: this makes it possible to reduce the resources and acquisition time of the image stack, but also the memory space to store the image stack, without impacting, or with very little impact on the quality with which the scene is imaged.

[0012] Following another example, when the scene is very complex, the number of images to be acquired can be increased: this allows to maintain a good quality to image the scene, using more resources.

[0013] According to another exemplary embodiment, when the acquisition device has a lot of resources, the number of images to be acquired can be increased.

[0014] According to another exemplary embodiment, when the acquisition device has few resources, for example a low battery or a very busy processor, the number of images to be acquired can be reduced.

[0015] Of course, these examples are by no means limiting. Other examples may combine multiple parameters, for example those listed above, to adjust the number of images acquired during the image acquisition step.

[0016] By image we mean a digital image, and in particular a raster image.

[0017] The at least one device parameter taken into account to adjust the number of images can be any type of parameter relating to the operation or state of the device.

[0018] According to embodiments, the at least one parameter relating to said device can be chosen from the following parameters: - battery level of said device: for example, the number of images acquired may be reduced when the battery level is low and / or increased when the battery level is high; - availability level of at least one processor of said device: for example, the number of images acquired can be reduced when the processor occupation level is high, and / or increased when the processor occupation level is low; - speed of movement of said device: for example, the number of images acquired can be reduced when the speed of movement of the device is low, and / or increased when the speed of movement of the device is high; - free memory space: for example, the number of acquired images can be decreased when the free memory space is small, and / or increased when the free memory space is large; - complexity of the optical transfer function of the optical lens, camera module or camera, i.e. the diversity of the point dispersion function PSF, or of the optical contrast function MTF: for example, the number of acquired images may be reduced when this diversity is low, in particular for the focus set when taking images, and / or increased when this diversity increases. - etc.

[0019] The value of each of these parameters is available in the device acquiring the images of the scene and can be read within said device.

[0020] By diversity of the PSF transfer function, we mean the following notion. For a given point in the scene, the optical transfer function is a surface representing the intensities obtained at the image sensor. For each point in the scene, in particular at the same distance from the sensor, we can have surfaces representing the same, close, or different intensities, which corresponds to a certain level of diversity from least to most. It may be useful to compare these surfaces after renormalization, that is to say after division by the total intensity detected on the whole surface, in order not to introduce into the notion of diversity the aspects of amplitude correction according to the position of the projected point on the sensor, but only the effects of spreading of the point.

[0021] The at least one scene parameter taken into account to adjust the number of images can be any type of parameter relating to the scene or the imaging conditions of the scene.

[0022] According to embodiments, the at least one parameter relating to the scene can be chosen from the following parameters: - brightness of said scene: for example, the number of images acquired can be reduced when the brightness of the scene is high and / or increased when the brightness of the scene is low; - complexity of said scene: for example, the number of images acquired can be reduced when the complexity of the scene is low, and / or increased when the complexity of the scene is high; - number of depth planes of said scene: for example, the number of acquired images can be reduced when the number of depth planes is low, and / or increased when the complexity of the scene is significant, in particular in the depth direction; - mobility of scene elements: for example, the number of images acquired can be reduced when the scene elements are static or not very mobile, and / or increased when the scene elements are very mobile; - etc.

[0023] According to non-limiting embodiments, the value of at least one of the parameters relating to the scene can be determined by taking a first image of the scene, called an exploratory image, before or at the start of the acquisition step. This (or these) exploratory image(s) can then be analyzed, during an analysis step, by known techniques, to determine the brightness of the scene, the complexity of the scene and the depth of the objects present in the scene, i.e. the number of planes in the depth direction of the scene, or even a notion of speed of the elements of the scene which can be obtained by comparing two images, or by analyzing data from another complementary sensor.

[0024] The value of at least one of the parameters relating to the scene can be determined / measured by other means. For example, the brightness of the scene can be measured by a sensor fitted to the device. The number of planes in the depth direction of the scene can also be determined from measurements made by at least one sensor, such as a lidar, a time-of-flight camera or other, fitted to the device and measuring the distance of objects in the scene in the depth direction.

[0025] Of course, all these techniques are given as non-limiting examples.

[0026] According to embodiments, the method according to the invention may further comprise a step of generating, by calculation, at least one image, called the estimated image, at a focus different from those of the acquired images, the stack of images comprising said estimated image.

[0027] The stored image stack may include said estimated image, possibly after processing said image.

[0028] The estimated image at a focus can be calculated from at least one image acquired, during the acquisition step, at a focus different from the focus of the estimated image.

[0029] The estimated image can be generated by computation using any known technique. For example, the estimated image can be computed using an optical transfer function of the camera module, in particular: - the optical transfer function of the camera module, or of the optical lens, corresponding to the focus of the at least one acquired image, and / or - the optical transfer function of the camera module, or of the optical lens, corresponding to the focus of the estimated image. For example, at least one optical transfer function can be the PSF.

[0030] Of course, the estimated image can be calculated by other techniques, and the example given above is not limiting.

[0031] According to embodiments, the method according to the invention may comprise a step of processing at least one, and in particular each, image of the stack of images before the storage step.

[0032] Thus, the at least one image stored in the image stack is the processed image.

[0033] At least one image may be processed to improve the quality of said image.

[0034] According to embodiments, for at least one image, the processing step may comprise a correction of geometric aberrations introduced into said image by the camera module during the acquisition of said image, and in particular of the geometric aberrations introduced into the image, compared to another image in the stack, due to the change of focus.

[0035] Such correction of geometric aberrations can be carried out using any known technique.

[0036] Such geometric aberration correction can be performed for one or more images in the stack, taking one of the images in the image stack as a reference.

[0037] Alternatively, such geometric aberration correction may be performed for one or more images in the stack, in particular for each of the images in the stack, by taking a reference image format as a reference. The reference image format may be the format of the optical sensor of the camera module used.

[0038] According to embodiments, for at least one image, the processing step may comprise correcting the sharpness of said image.

[0039] Such sharpness correction can be achieved using any known technique.

[0040] Such sharpness correction may be performed using at least one optical transfer function, such as a PSF, of the camera module or the optical lens. For at least one image, the optical transfer function used to perform this correction may be specific to the focus with which said image was captured.

[0041] According to embodiments, for at least one image, the processing step may comprise a correction of chromatic aberrations introduced into said image by the camera module during the acquisition of said image.

[0042] Such correction of chromatic aberrations can be carried out using any known technique.

[0043] Such correction of chromatic aberrations may be performed using at least one optical transfer function, such as for example a PSF, of the camera module or the optical lens. For at least one image, the optical transfer function used to make this correction may be specific to the focus with which said image was captured.

[0044] According to embodiments, the number of images can be determined with at least one predetermined law taking as input the value of the at least one parameter.

[0045] According to embodiments, a predetermined law may be used for all of the parameters. In this case, the predetermined law may take as input the values ​​of all of the parameters and provide a single value as output. The number of images may correspond to said provided value. In the case where the provided value is not an integer, the number of images may be the integer closest to the provided value, or the integer immediately greater than the provided value.

[0046] According to embodiments, different laws may be used for at least two parameters, and in particular each of the parameters. In this case, the law used for each of said parameters takes as input the value of said parameter and provides an individual output value. The value corresponding to the number of images may then be determined as a function of all the individual output values, for example by averaging, possibly after applying different weights for at least two of the parameters. In the case where the value thus obtained is not an integer, the number of images may be the integer closest to said value, or the integer immediately greater than said value.

[0047] According to embodiments, the, or at least one, predetermined law may be a predetermined mathematical relationship.

[0048] According to embodiments, the, or at least one, predetermined law may be a correspondence table.

[0049] According to embodiments, the, or at least one, law can be predetermined by tests or calibration.

[0050] According to embodiments, the, or at least one, predetermined law may be a pre-trained model, such as a neural network.

[0051] According to embodiments, for at least one parameter, the number of images can be given by the value of said parameter.

[0052] For example, in the case where the parameter is the number of planes in the depth direction of the scene, then the number of images to be acquired can correspond to said number of planes.

[0053] According to another aspect of the invention, there is provided a computer program comprising executable instructions which, when executed by a computing device, implement all the steps of the method according to the invention.

[0054] The computer program can be in any computer language, such as machine language, C, C++, JAVA, Python, etc.

[0055] The computer program can be stored on any type of computer media.

[0056] According to another aspect of the invention, a device is proposed comprising means configured to implement all the steps of the method according to the invention.

[0057] The device according to the invention can be, or be integrated into, any type of device such as a smartphone, a tablet, a computer, a headset, a virtual reality headset, an augmented reality headset, a medical imaging device, a camera, a video camera, etc.

[0058] The device according to the invention can be, or be integrated into, any type of vehicle, such as a land vehicle, a flying vehicle, a drone, a maritime vehicle, etc., autonomous or not.

[0059] According to another aspect of the invention, there is provided an apparatus comprising: - at least one camera module, and - at least one computing unit; configured to implement the steps of the method according to the invention.

[0060] In particular, the device may be: - a smartphone, - a tablet, - a computer, - a television, - a headset, in particular a virtual reality headset or an augmented reality headset, - a medical imaging device, in particular an endoscope, an ultrasound device, etc.

[0061] Of course, the invention is not limited to the examples of devices which have just been given.

[0062] According to another aspect of the present invention, there is provided a vehicle comprising: - at least one camera module, and - at least one computing unit; configured to implement all the steps of the method according to the invention.

[0063] The vehicle according to the invention may be a land vehicle, such as for example a car.

[0064] The vehicle according to the invention may be a maritime vehicle, such as for example a boat, a submarine, etc.

[0065] The vehicle according to the invention may be a flying vehicle, such as for example an airplane, a helicopter, a drone, etc.

[0066] Of course, the invention is not limited to the examples of vehicles which have just been given. Description of figures and embodiments

[0067] Other advantages and characteristics will appear on examining the detailed description of non-limiting embodiments, and the attached drawings in which: - FIGURE 1 is a schematic representation of a non-limiting exemplary embodiment of a method according to the invention; - FIGURE 2 is a schematic representation of a non-limiting exemplary embodiment of a device according to the invention; - FIGURE 3 is a schematic representation of a non-limiting exemplary embodiment of an apparatus according to the invention; - FIGURE 4 is a schematic representation of another non-limiting exemplary embodiment of an apparatus according to the invention; - FIGURE 5 is a schematic representation of another non-limiting exemplary embodiment of an apparatus according to the invention; and - FIGURE 6 is a schematic representation of a non-limiting exemplary embodiment of a vehicle according to the invention.

[0068] It is understood that the embodiments which will be described below are in no way limiting. In particular, it is possible to imagine variants of the invention comprising only a selection of characteristics described below isolated from the other characteristics described, if this selection of characteristics is sufficient to confer a technical advantage or to differentiate the invention compared to the state of the prior art. This selection includes at least one preferably functional characteristic without structural details, or with only part of the structural details if it is this part which is only sufficient to confer a technical advantage or to differentiate the invention from the prior art.

[0069] In particular, all the variants and embodiments described can be combined with each other if there is no technical obstacle to this combination.

[0070] In the figures and in the rest of the description, the elements common to several figures retain the same reference.

[0071] FIGURE 1 is a schematic representation of a non-limiting exemplary embodiment of a method according to the present invention.

[0072] The method 100 of FIGURE 1 may be used for acquiring a stack of images of a scene, the stack of images comprising several images acquired at different focuses such that each image comprises a different area of ​​sharpness for said scene.

[0073] The image stack may be acquired with at least one camera module. The, or each, camera module may include at least one image sensor, such as a CCD or CMOS sensor, associated with at least one lens. The camera module may include, or be associated with, a mechanism for changing the focus of the camera module, for example by changing the distance between the optical lens and the image sensor, or by any other method.

[0074] The method 100 comprises a step 102 of determining the value of at least one parameter making it possible to calculate the number of images which will be captured by the camera module.

[0075] This step 102 may comprise a step 104 of determining the value of at least one parameter relating to the device used for acquiring images of the scene. The at least one parameter relating to the device may be any combination of at least one of the following parameters: - battery level of said device, - availability level of at least one processor of said device, - speed of movement of said device, - free memory space in said device; - complexity of the transfer function, at least at one of the distances at which the elements of the scene are located, or at a probable distance at which they are located, - etc. The value of each of these parameters is normally available in the device acquiring the images of the scene and can therefore be read within said device. Alternatively, or in addition, the value of at least one of these parameters can be provided by a software agent installed in said device or a sensor integrated into said device.

[0076] Alternatively, or in addition, step 102 may comprise a step 106 of determining the value of at least one parameter relating to the scene. The at least one parameter relating to the scene may be any combination of at least one of the following parameters: - brightness of said scene, - complexity of the said scene, - number of depth shots of said scene, - speed of movement of the scene elements, - etc.

[0077] The value of at least one of the parameters relating to the scene can be determined by taking at least one image of the scene, called an exploratory image, during step 106, or before step 106. This exploratory image can then be analyzed, by known techniques, to determine: - the brightness of the scene: for example, the brightness can be determined by calculating the average energy in the exploratory image from the values ​​of the pixels making up said exploratory image; - scene complexity: scene complexity can be determined by detecting the number of objects in the scene, using known techniques, for example using the YOLOv5 model; - the number of planes in the depth direction of the scene: the number of planes in the scene can be determined by detecting the depth of objects in the scene, using known techniques. - the speed of movement of objects in the scene can be determined by comparing at least two different images, or by analyzing data from another complementary sensor,

[0078] Alternatively, or in addition, the value of at least one of the parameters relating to the scene can be measured by sensors equipping the device, such as for example: - a brightness sensor to measure the brightness of the scene; - a lidar, a time-of-flight camera or any other sensor, to detect the presence of objects in the scene and measure their depth; - possibly deduce additional travel information, - etc.

[0079] Thus, step 102 provides for each parameter relating to the device and / or for each parameter relating to the scene, a value at the time, or just before, imaging the scene.

[0080] The method 100 comprises a step 108 for determining the number of images to be acquired, as a function of the at least one value provided by the step 102, using a predetermined law taking said at least one value as input.

[0081] The predetermined law may be a mathematical relationship taking as input the at least one value provided by step 102. The predetermined law may be a correspondence table linking the at least one value provided by step 102 to a number of images to be acquired.

[0082] The predetermined law may be a pre-trained model, such as a pre-trained neural network taking as input the at least one value provided by step 102.

[0083] Of course, the invention is not limited to these examples.

[0084] If the predetermined law provides a value that is not an integer, then the number of images to be acquired may be the nearest integer, or the next higher integer, to said value. This latter embodiment ensures that the scene is imaged with a quality equal to or higher than that desired.

[0085] For example, for each parameter, a predetermined law may give a number of images to be acquired for the value provided by step 102. If several parameters are taken into account, the final value of the number of images may be an average of each of the values ​​obtained for these parameters, possibly after applying different weights, and possibly after rounding to the nearest or immediately higher integer.

[0086] The method 100 comprises a step 110 during which the scene is imaged. During this step 110, the camera module of the apparatus is commanded to acquire as many images of the scene as the number determined during step 108. At least two, and in particular all the images, are acquired at different focuses so that each of said images comprises a different area of ​​sharpness for the scene. Such an acquisition can be carried out by focus bracketing.

[0087] Thus, step 110 provides a stack of acquired images, at least two, and in particular, all images are acquired at different focuses and have different sharpness areas for the scene.

[0088] The method 100 may optionally comprise a step 112 for generating, by calculation, an image of the scene, and in particular an image of the scene at a focus different from the focus of the images acquired during step 110.

[0089] Such generation by calculation of an image of the scene can be carried out from one, or more, of the images acquired during step 110.

[0090] Such generation by calculation of an image of the scene can for example take into account optical transfer functions, and in particular PSFs, of the camera module or the optical lens, and even more particularly: - the PSF of the camera module, or of the optical lens, corresponding to the desired focus for the image generated by calculation, and - the PSF of the camera module, or of the optical lens, corresponding to the focus of the, or of each, image acquired in step 110 and from which the image is generated.

[0091] The image(s) generated by calculation in step 112 may be added to the stack of images obtained in step 110. Thus, step 112 provides a stack of enriched images of the scene.

[0092] The method 100 may optionally comprise a step 114 for processing at least one of the images in the stack of images of the scene.

[0093] For at least one image, such processing may be of any type. In particular, for at least one image, such processing may include correction: - geometric aberration(s) in said image, and / or - chromatic aberration(s) in said image. Known aberration correction techniques can be used.

[0094] For at least one image, such processing may include enhancing the sharpness of the image, using known techniques.

[0095] Thus, step 114 provides a stack of images of which at least one has been processed.

[0096] In a step 116, the stack of images of the scene is stored on a storage medium, dedicated or not. In particular, the storage medium is located in the device. Alternatively, the storage medium may be located remotely from the device, in particular in the cloud.

[0097] FIGURE 2 is a schematic representation of a non-limiting exemplary embodiment of a device according to the present invention.

[0098] The device 200 of FIGURE 2 can be used to implement a method according to the invention and in particular the method 100 of FIGURE 1, to take a stack of images of the scene with a number of images adjusted according to the device used for image acquisition and / or the imaged scene.

[0099] The device 200 acquires a stack of images of the scene with a camera module 202.

[0100] The camera module 202 includes an optical lens 204 having one or more optical elements 206, such as a lens, a spacer, etc.

[0101] The camera module 202 further comprises an image sensor 208, associated with the optical lens 204. The image sensor 208 can be any type of sensor such as a CCD or CMOS sensor.

[0102] The camera module 202 further comprises, or is associated with, a mechanism (not shown) for modifying the focus of the camera module 202, for example by modifying the distance between the optical lens 204 and the image sensor 208, by any other technique.

[0103] The camera module 202 may be part of the device 200. Alternatively, the camera module 202 may not be part of the device 200, in particular when the device 200 is implemented in an apparatus already having at least one camera module.

[0104] The device 200 further comprises a computing unit 210.

[0105] The calculation unit 210 comprises a module 212 for determining the value of at least one parameter relating to the apparatus performing the image acquisition, and / or the value of at least one parameter relating to the imaged scene. This module 212 can cooperate with one or more sensors (not shown), in particular to determine the value of at least one parameter relating to the scene, such as a brightness sensor, a lidar, a time-of-flight camera, etc. This module 212 can comprise a module for analyzing at least one image, called an exploratory image, of the scene taken by the camera module to determine the value of at least one parameter relating to the scene. This module 212 can in particular be configured / programmed to carry out step 102 of the method 100.

[0106] The calculation unit 210 comprises a module 214 for determining the number of images of the scene to be acquired, as a function of the value of at least one parameter provided by the module 212. This module 214 can execute a previously trained model, use at least one predetermined law, or a previously determined correspondence table, to determine the number of images. This module 214 can in particular be configured / programmed to carry out step 102 of the method 100.

[0107] The calculation unit 210 comprises a module 216 for acquiring images of the scene, depending on the number determined by the module 214. This module 216 has the function of controlling the camera module 202, possibly via an imaging application, to acquire as many images of the scene than the number provided by the module 214. Preferably, this module 216 indicates to the camera module 202, directly or via an imaging application, the focus corresponding to each image to be acquired. This module 216 can in particular be configured / programmed to carry out step 110 of the method 100.

[0108] The calculation unit 210 may optionally comprise a module 218 for generating, by calculation, an image of the scene, at a focus different from the focus of the acquired images, from at least one of the acquired images. This module 218 may in particular be configured / programmed to carry out step 112 of the method 100.

[0109] The calculation unit 210 may optionally comprise a module 220 for processing at least one image of the scene, in particular at least one acquired image and / or at least one image generated by calculation, for example to correct aberrations introduced into said image by the camera module 202, and / or to improve the sharpness of said image. This module 220 may in particular be configured / programmed to carry out step 114 of the method 110.

[0110] The calculation unit 210 further comprises a module 222 for storing, in a storage medium 224, a stack of images of the scene. The stored stack of images comprises: - all or part of the acquired images, and - optionally at least one image generated by calculation; optionally after processing. This module 222 can in particular be configured / programmed to carry out step 116 of the method 110. [YES] The storage medium 224 can be any type of medium, such as for example a memory card, integrated or not into the device used for the acquisition of the stack of images.

[0112] The storage medium 224 may be local or remote.

[0113] The storage medium 224 may be part of the device 200, as shown in the example of FIGURE 2. Alternatively, the storage medium 224 may not be part of the device 200, particularly when the device 200 is implemented in an apparatus already having a storage medium, or when the storage medium is located remote from the apparatus used to image the scene.

[0114] At least one of the modules 212-222 may be an independent module of the others.

[0115] At least two of the modules 212-222 can be integrated within the same module.

[0116] At least one of the modules 212-222, and / or the computing unit 210, may be a hardware module.

[0117] At least one of the modules 212-222, and / or the computing unit 210, may be a software module, such as a computer program.

[0118] At least one of the modules 212-222, and / or the computing unit 210, may be a combination of at least one software module, such as a computer program, and at least one hardware module.

[0119] In particular, at least one of the modules 212-222, and / or the calculation unit 210, can be integrated into an electronic chip, or even into an application installed in the user device used to image the scene.

[0120] FIGURE 3 is a schematic representation of a non-limiting exemplary embodiment of an apparatus according to the present invention.

[0121] The apparatus 300 of FIGURE 3 comprises means configured to implement the invention, and in particular the method 100.

[0122] The apparatus 300 may comprise a device according to the invention, and in particular the device 200 of FIGURE 2.

[0123] In the example shown in FIGURE 3, the device 300 is a smartphone, or a tablet, comprising the device 200 of FIGURE 2.

[0124] FIGURE 4 is a schematic representation of another non-limiting exemplary embodiment of an apparatus according to the present invention.

[0125] The apparatus 400 of FIGURE 4 comprises means configured to implement the invention, and in particular the method 100.

[0126] The apparatus 400 of FIGURE 4 may comprise a device according to the invention, and in particular the device 200 of FIGURE 2.

[0127] In the example shown in FIGURE 4, the apparatus 400 is a headset intended to be worn by a user, in particular a virtual reality, VR, headset, or an augmented reality headset, comprising the device 200 of FIGURE 2.

[0128] FIGURE 5 is a schematic representation of a non-limiting exemplary embodiment of an apparatus according to the present invention.

[0129] The apparatus 500 of FIGURE 5 comprises means configured to implement the invention, and in particular the method 100.

[0130] The apparatus 500 of FIGURE 5 may comprise a device according to the invention, and in particular the device 200 of FIGURE 2.

[0131] In the example shown in FIGURE 5, the apparatus 500 is a medical imaging apparatus, such as an endoscope, an ultrasound apparatus, etc. comprising the device 200 of FIGURE 2. In particular, the medical imaging apparatus 500 comprises a camera module 202 provided in the distal portion of an optical tube.

[0132] FIGURE 6 is a schematic representation of a non-limiting exemplary embodiment of a vehicle according to the present invention.

[0133] The vehicle 600 of FIGURE 6 comprises means configured to implement the invention, and in particular the method 100.

[0134] The vehicle 600 of FIGURE 6 may comprise a device according to the invention, and in particular the device 200 of FIGURE 2.

[0135] In the example shown in FIGURE 6, the vehicle 600 is a land vehicle, in particular a car, comprising the device 200 of FIGURE 2. In particular, the vehicle 600 comprises a camera module 202, for example arranged behind the front windshield of the vehicle 600.

[0136] Of course, the invention is not limited to the examples which have just been described.

Claims

CLAIMS 1. Method (100) for acquiring an image stack (PIL) of a scene with at least one camera module (202) of a device, said method comprising the following steps: - acquisition (110) of several images of said scene, at least two of said images being acquired at different focuses, and - storage (116) of a stack of images comprising at least a portion of said acquired images; characterized in that it further comprises, prior to the acquisition step (110), a step (102) of adjusting the number of images to be acquired as a function of at least one parameter relating to said apparatus and / or at least one parameter relating to said scene.

2. Method (100) according to the preceding claim, characterized in that the at least one parameter relating to said device is chosen from the following parameters: - battery level of said device, - availability level of at least one processor of said device, - speed of movement of said device, - free memory space, - complexity of the optical transfer function of the camera module, - etc.

3. Method (100) according to any one of the preceding claims, characterized in that the at least one parameter relating to the scene is chosen from the following parameters: - brightness of said scene, - complexity of said scene, and the number of depth shots of said scene, - number of depth shots of said scene, - mobility of scene elements, - etc.

4. Method (100) according to any one of the preceding claims, characterized in that it comprises a step (112) of generating, by calculation, at least one image, called the estimated image, at a focus different from those of the acquired images, the image stack comprising said estimated image.

5. Method (100) according to any one of the preceding claims, characterized in that it comprises a step (114) of processing at least one, and in particular each, image of the stack of images before the storage step.

6. Method (100) according to the preceding claim, characterized in that, for at least one image, the processing step (114) comprises a correction of geometric aberrations introduced into said image by the camera module (202) during the acquisition of said image.

7. Method (100) according to any one of claims 5 or 6, characterized in that, for at least one image, the processing step (114) comprises a correction of the sharpness of said image, for example by using a point spread function, PSF (for “Point Spread Function”).

8. Method (100) according to any one of claims 5 to 7, characterized in that, for at least one image, the processing step (114) comprises a correction of chromatic aberrations introduced into said image by the camera module (202) during the acquisition of said image.

9. A computer program comprising executable instructions which, when executed by a computing device, implement all the steps of the method (100) according to any one of the preceding claims.

10. Stack of images (PIL), stored on a computer medium, obtained by the method (100) according to any one of claims 1 to 8.

11. Acquisition device (200) comprising means configured to implement all the steps of the method according to any one of claims 1 to 8.

12. Apparatus (300;400;500) comprising: - at least one camera module (202), and - at least one computing unit (210); configured to implement the method (100) according to any one of claims 1 to 8.

13. Apparatus (300; 400; 500) according to the preceding claim, characterized in that it is: - a smartphone (300), - a tablet (300) - a computer, - a television, - a headset (400), in particular a virtual reality headset or augmented reality headset, or - a medical imaging device (500).

14. Vehicle (600) comprising: - at least one camera module (202), and - at least one computing unit (210); configured to implement the method (100) according to any one of claims 1 to 8.

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