Method for obtaining a stack of images, and computer program product implementing such a method

Abstract

The invention relates in particular to a method for obtaining a stack (P) of images (IC) from an input image (1) dependent on a raw image acquired by a sensor (6) of a camera module (4) through an optical system (7) of the camera module (4), the raw image being representative of a scene (5), the method being implemented by computer and being characterised in that it comprises, for each part of a plurality of separate parts of the input image (1), the following steps: a) determining a corresponding optical transfer function (PSF) between a part (Z) of the scene (7) associated with the part of the input image (1), b) deconvoluting (D) the input image (1), using the determined optical transfer function, to obtain a corrected image (IC), so as to obtain a plurality of corrected images (IC), the plurality of corrected images obtained forming the stack (P) of images.

Description

[0001] METHOD FOR OBTAINING A STACK OF IMAGES, PROGRAMMED PRODUCT

[0002] COMPUTER SYSTEM IMPLEMENTING SUCH A METHOD

[0003] FIELD OF THE INVENTION

[0004] The present invention relates to a method for obtaining an image stack from an input image (or an acquired image).

[0005] The invention also relates to a computer program product which implements such a device, and to an electronic apparatus comprising such a computer program product.

[0006] The invention applies to the field of digital images, in particular digital cameras and digital cameras, for example for telephones, tablets or laptops.

[0007] It can also be applied to images that would be reproduced in a motor vehicle or a flying device, for example to show the driver or pilot specific visual information, or to provide a driving or autonomous piloting system with sharper images.

[0008] It can also be applied in the medical field, for example to restore images from an observation of a medical diagnosis.

[0009] It can also be applied in the field of video surveillance to restore sharper images with a great depth of field.

[0010] STATE OF THE ART

[0011] When an image is acquired, some areas of the image are sharper than others: the sharpness of an area of ​​an acquired and restored image is obtained by taking into consideration the distance at which the image of an object is taken and by adjusting the camera lens according to this distance: thus, what is around the object in the acquired image is less sharp than what is in the area corresponding to the lens setting.

[0012] To obtain an image where all areas appear sharp, it is necessary to vary the focusing conditions for each area appearing in the image: we then obtain a stack of images with different sharp areas from one image to another: this is a stack of images with focus shift.

[0013] We then understand that we must take as many images as there are objects in the image, adjusting the focal length of the camera each time, so that each of the objects in the image is rendered sharp in an image dedicated to it.

[0014] Another problem also arises: when the object is moving, it is necessary to modify the scene observed during the image capture with a spread over time to vary the focusing conditions.

[0015] An aim of the present invention is to improve the restitution of an image, by ridding the sharpness zone of certain aberrations.

[0016] Another aim of the invention is to propose a method which does not require acquiring several images so that each object can appear clear (which is important in particular when, in the scene, the objects are moving)

[0017] STATEMENT OF THE INVENTION

[0018] To this end, according to a first aspect, the invention relates to a method for obtaining a stack of images from an input image dependent on a raw image acquired by a sensor of a camera module through an optical system of the camera module, said raw image being representative of a scene, the method being implemented by computer and being characterized in that it comprises, for each of a plurality of distinct parts of said input image, the following steps: a) determining a corresponding optical transfer function between a part of the scene associated with the part of the input image, b) deconvolution, by the determined optical transfer function, of the input image to obtain a corrected image, so as to obtain a plurality of corrected images, said plurality of corrected images obtained forming said stack of images.

[0019] Using such a method, it is thus possible to obtain a stack of images among which each corrected image comprises a distinct, clear part of the scene represented by the input image (obtained from the raw image, the input image being able to correspond to the raw image if the raw image has not undergone any transformation).

[0020] In other words, from an input image representing a scene, we can obtain a plurality of images where each represents at least part of the scene clearly.

[0021] Advantageously, step a) is carried out by determining, in said input image, a part of the scene where objects are all located substantially at the same distance from the sensor.

[0022] Furthermore, step a) consists of producing a mesh of the input image leading to image portions and associating with each portion an average optical transfer function between the corresponding part of the scene and the sensor of the camera module.

[0023] Preferably, step b) is carried out by composition by the optical transfer function in the frequency domain.

[0024] According to an alternative embodiment, step b) is carried out by composition with a correction function derived from the optical transfer function.

[0025] Advantageously, furthermore, the method also comprises the following steps for each of said plurality of corrected images forming said image stack:

[0026] - cutting said corrected image into distinct parts of said input image which correspond to the parts of the scene of the identified optical transfer functions,

[0027] - assigning a sharpness index to each distinct part of said input image by applying an image sharpness estimator, and

[0028] - creating a sharpness index map associated with each of said plurality of corrected images.

[0029] According to an alternative implementation, the method further comprises the following steps, for each of said plurality of corrected images forming said stack of images:

[0030] - cutting said corrected image into distinct parts of said input image acquired by said sensor which correspond to the parts of the scene of the identified optical transfer functions,

[0031] - assigning a parameter representing a distance between said optical system and an object in each distinct part of said input image using the optical transfer function identified for the scene part corresponding to the distinct image part, and

[0032] - creation of said focal length map associated with each of said plurality of corrected images.

[0033] In the case where the method makes it possible to create a sharpness index map associated with each of said plurality of corrected images, as indicated above, it may further comprise the following steps:

[0034] - receiving an attention position in the image, by choosing a distinct image part in said input image,

[0035] - identification, in said stack of images, of the corrected image in said stack of images where the part of the scene, corresponding to the position of attention in the image, is the sharpest, presenting a maximum value of said sharpness index,

[0036] - selection in the identified corrected image, of the attention position, and positioning in a reconstructed image, of the selected attention position, said steps being repeated as many times as necessary so that the reconstructed image includes all the attention positions of all the images in the stack of images, the reconstructed image thus corresponding to said dynamic visualization of the input image.

[0037] The invention also relates to a computer program comprising executable instructions which, when executed by computer, implement the steps of the method as defined above.

[0038] The computer program can be in any computer language, such as machine language, C, C++, JAVA, PYTHON etc.

[0039] According to another aspect, the invention relates to an image processing device for an input image from a camera module, the input image being representative of a scene acquired by a sensor of the camera module through an optical system of the camera module, the processing device being configured to a) determine a corresponding optical transfer function between a part of the scene associated with the part of the input image, b) perform the deconvolution, by the determined optical transfer function, of the input image to obtain a corrected image, so as to obtain a plurality of corrected images, said plurality of corrected images obtained forming said stack of images.

[0040] Advantageously, the invention also relates to an electronic device comprising a camera module and an image processing device as defined above.

[0041] DEVICE

[0042] 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.

[0043] 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 calculator, a processor, a computer chip, programmed to implement the method according to the invention, for example by executing the computer program according to the invention.

[0044] DEVICE

[0045] According to another aspect of the invention, there is provided an apparatus comprising: - a means for displaying an image, - at least one means for detecting a target position, and - at least one calculation means; configured to implement all the steps of the method according to the invention.

[0046] DEVICE COMPRISING A CAMERA

[0047] The device may not include an image acquisition means. In this case, the device is used to display one or more images acquired by another device.

[0048] Alternatively, the apparatus may comprise an image acquisition means, such as a camera or a camera module. In this case, the apparatus may be used to display one or more images acquired by said apparatus or by another apparatus.

[0049] SMARTPHONE OR TABLET TYPE DEVICE In particular, the device may be a user device of the Smartphone, tablet, etc. type comprising a display screen. In this case, the detection means may be or may comprise the touch surface, in particular integrated into, or associated with, the display screen of said device.

[0050] COMPUTER-TYPE USER DEVICE

[0051] In particular, the device may be a computer-type user device, comprising a display screen. In this case, the detection means may be or may comprise a touch-sensitive surface, in particular integrated into, or associated with, the display screen of said computer, or a pointer moved for example by a mouse, or a directional pad of said computer.

[0052] TELEVISION TYPE DEVICE

[0053] In particular, the device may be a television. In this case, the detection means may be a camera integrated into said television, detecting the gaze and the position of the head of the observer, or a pointer moved for example by a remote control of said television.

[0054] VIRTUAL REALITY HEADSET TYPE DEVICE

[0055] In particular, the device may be a virtual reality or augmented reality headset comprising a display screen or a projector associated with a projection surface onto which each image is projected. In this case, the detection means may be or may comprise a sensor, in particular an optical sensor, equipping said headset.

[0056] Of course, the apparatus according to the invention is not limited to the examples which have just been given.

[0057] MEDICAL IMAGING DEVICE TYPE APPARATUS

[0058] In particular, the device may be a medical imaging device.

[0059] In particular, the device may be an endoscope, an ultrasound device, etc. VEHICLE

[0060] According to another aspect of the present invention, there is provided a vehicle comprising:

[0061] - a means of displaying an image, and

[0062] - at least detection of a target position,

[0063] - at least one calculation means; configured to implement all the steps of the method according to the invention.

[0064] VEHICLE INCLUDING A CAMERA

[0065] The vehicle may not include a means of image acquisition. In this case, the image(s) of the scene are provided by another device or another vehicle.

[0066] Alternatively, the vehicle may comprise an image acquisition means, such as a camera or a camera module. In this case, the image(s) of the scene are captured by said image acquisition means, or provided by another device or another vehicle.

[0067] EXAMPLE OF VEHICLE

[0068] According to embodiments, the vehicle may be a land vehicle, such as a car, autonomous or not.

[0069] According to embodiments, the vehicle may be a flying vehicle, such as a drone, an airplane, a helicopter, autonomous or not.

[0070] According to embodiments, the vehicle may be a maritime vehicle, such as a boat or a submarine, autonomous or not.

[0071] IMAGE TYPE

[0072] 2D IMAGE

[0073] According to embodiments, at least one image of the scene is a 2D image. In particular, the current image is a 2D image. In particular, the new image is a 2D image.

[0074] If applicable, the image stack includes at least one 2D image. In particular, every image in the image stack is a 2D image. If applicable, the image that is sharp everywhere is a 2D image.

[0075] 3D IMAGE

[0076] According to embodiments, at least one image of the scene is a 3D image. In particular, the current image is a 3D image. In particular, the new image is a 3D image.

[0077] If applicable, the image stack includes at least one 3D image. In particular, every image in the image stack is a 3D image.

[0078] If the image is clear everywhere, it is a 3D image.

[0079] BRIEF DESCRIPTION OF THE FIGURES

[0080] The invention will be better understood on reading the description which follows, given solely as a non-limiting example and made with reference to the appended drawings in which:

[0081] [Fig. 1] is a schematic representation of the steps of implementing a method according to a first embodiment of the invention,

[0082] [Fig. 2] is a schematic representation of the steps of implementing a method according to a second embodiment of the invention

[0083] [Fig. 3] is a schematic representation of the steps of implementing a method according to a third embodiment of the invention,

[0084] [Fig. 4] illustrates, schematically, additional steps of a method according to the invention,

[0085] [Fig. 5] illustrates an alternative implementation to that illustrated in Figure 4, and [Fig. 6] is a schematic representation of an apparatus according to the invention positioned in front of a scene.

[0086] [Fig. 7] is a schematic representation of a non-limiting exemplary embodiment of a device according to the invention;

[0087] [Fig. 8a - 8c]: Figures 8a to 8c are schematic representations of non-limiting exemplary embodiments of an apparatus according to the invention; and [Fig. 9] is a schematic representation of a non-limiting exemplary embodiment of a vehicle according to the invention.

[0088] 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 compared to the state of the prior art.

[0089] In particular, all the variants and embodiments described can be combined with each other if there is no technical obstacle to this combination.

[0090] In the figures and in the rest of the description, the elements common to several figures retain the same reference.

[0091] DETAILED DESCRIPTION

[0092] Figure 1 schematically illustrates the steps of a method according to the invention.

[0093] We consider a camera module, for example a camera module of a smartphone type device, equipped with an optical system and a sensor, allowing the acquisition of a raw image.

[0094] For example, this is a raw image taken by a smartphone, the raw image representing a scene.

[0095] The raw image can be modified or not by applying gain corrections, black levels, pixels, etc.: we thus acquire an input image 1 referenced in figure 1, corresponding either to the raw image, or to the raw image to which corrections have been applied.

[0096] The scene represented in input image 1 is considered to comprise several parts ZI to ZN: this may correspond to parts of the scene which are located at different distances from the sensor of the camera module.

[0097] Each part ZI to ZN comprises its own optical transfer function PSF. As illustrated in Figure 1, the first step of the method consists in determining the optical transfer function of a part Z of the input image 1: thus, for N parts Z of the input image 1, N optical transfer functions will be identified: this corresponds to the references PSF (Zl), PSF (Z2), PSF (ZN) of Figure 1.

[0098] Then, for a part Zl of the image, presenting an optical transfer function PSF (Zl), we will carry out a deconvolution D of the input image 1 by the determined optical transfer function, to obtain a corrected image ICI.

[0099] For a second part Z2 of the image, presenting an optical transfer function PSF (Z2), we will carry out a deconvolution D of the input image 1 by the determined optical transfer function, to obtain a corrected image IC2.

[0100] And so on for the third part of image Z3, up to the Nth part of image ZN, so as to obtain corrected images IC3 to ICN.

[0101] For an RGB (Red, Green, Blue) color input image, it should be understood that it can be the image directly from the sensor (therefore the raw image presented above) or an image directly from the R, Gl, G2, B sites of each so-called Bayer matrix, or even a so-called debayered image, that is to say an image interpolated with respect to the R, Gl, G2 B sites of the detection matrix.

[0102] Subsequently, we will speak of PSF function to designate the three optical transfer functions PSFR, PSFV and PSFB which associate with each region of the scene the projection of the latter on the three wavelength bands of the sensors.

[0103] Furthermore, it should be noted that the deconvolution D can be performed by composition by the optical transfer function in the frequency domain, which requires the least computation. This is an algorithmic loop deconvolution.

[0104] Alternatively, it is also possible to perform deconvolution by composition with a correction function derived from the optical transfer function: for example, the technique can be of the Wiener filter type.

[0105] We thus obtain a quantity N of corrected images (or a plurality of corrected images) ICI to ICN, which form what we define as being a stack P of images. To determine the parts N of the input image 1 that we will consider to apply the method according to the invention, several solutions are retained:

[0106] In the input image 1, we can determine a part of the scene where objects are all located at approximately the same distance d from the sensor: Figure 2 schematically illustrates this choice: on the input image 1, we estimate the distance d at which an object in the scene is located and we determine the optical transfer function PSF corresponding to the objects located at the distance d.

[0107] The first objects all located at the same first distance from the sensor are referenced as being objects located at a distance dl, the objects all located at a second same distance from the sensor are referenced as being objects located at a distance d2 from the sensor ... the objects all located at an i-th distance from the sensor are referenced at a distance di.

[0108] Figure 4 illustrates an implementation where, for example, five areas are identified in the input image 1, each of the areas having a substantially uniform or uniform optical transfer function.

[0109] By applying the method according to the invention, a stack P of five corrected images ICI to IC5 is obtained.

[0110] For the corrected image ICI, deconvolution D took into consideration the optical transfer function of the objects in the scene located in zone 1.

[0111] For the corrected image IC2, the deconvolution D took into consideration the optical transfer function of the objects in the scene located in zone 2.

[0112] And so on until you have five corrected images.

[0113] According to an implementation variant, it can be provided that the first step, aimed at determining parts of image Z, consists of producing a mesh of the input image 1, leading to image portions and associating with each portion an average optical transfer function between the corresponding part of the scene and the sensor of the camera module.

[0114] Figure 5 illustrates an example of this mode of implementation: the mesh produced on the input image 1 is regular. It should be understood that other meshes could be produced without departing from the scope of the invention. Figure 3 schematically illustrates that the first portion of the mesh is considered to be zone 1 of the input image 1 taken at a distance di from the sensor of the optical system.

[0115] For each zone 1, 2, ... K (K corresponding to the number of portions defined in the mesh), we identify the average optical transfer function PSF(di) of the zone considered to carry out the deconvolution of the image so as to obtain the corrected image from the zone 1 considered. There will be as many corrected images in the image stack P as there are zones defined by the mesh.

[0116] According to an advantageous aspect of the invention, the method may comprise an additional step to that aimed at creating the stack of images: for each of the corrected images IC forming said stack of images, it may be provided to cut into distinct parts which correspond to the parts of the scene of the identified optical transfer functions.

[0117] Then, a parameter representing a distance between said optical system and an object in each distinct part of said input image is assigned using the optical transfer function identified for the scene part corresponding to the distinct image part.

[0118] Finally, a map of focal lengths associated with each of said plurality of corrected images is created: this provides a depth map.

[0119] Figure 1 shows that the method according to the invention may include additional steps, in particular to produce a map by CNET sharpness indexing of the parts of images from which the corrected images were generated.

[0120] For each image in the stack P of corrected images IC, we will divide the corrected image into distinct parts of said input image which correspond to the parts of the scene of the identified optical transfer functions.

[0121] Then, we will assign a sharpness index to each distinct part of said input image by applying an image sharpness estimator: this step corresponds to the NET reference in figure 1.

[0122] Finally, a sharpness index map CNET is created, which is associated with each of said plurality of corrected images ICI to ICN. From this sharpness map CNET, as shown in Figure 5, an attention position (x, y)l in the image is further received (for example, on the corrected image ICI in Figure 5, by choosing a distinct image portion in said input image 1. where the portion of the scene corresponding to the attention position (x, y)l in the image is the sharpest (corresponding to the reference (x, y)l NET), having a maximum value of said sharpness index.

[0123] Finally, we select in the corrected image identified HERE the attention position (x, y)l NET, and positioning in a reconstituted image IR (the reference R corresponds to an image reconstruction step), the attention position (x,y)l selected,

[0124] This step is repeated as many times as necessary so that the reconstituted IR image includes all the attention positions (x, y)n NET of all the corrected IC images of the stack P of images, the reconstituted IR image thus corresponding to said dynamic visualization of the input image.

[0125] Another application of the invention: Figures 4 and 5 each also illustrate the possibility of reconstructing an image from the stack P of corrected images IC.

[0126] Figure 4 shows an example where concentric zones are identified on the input image 1, an optical transfer function being determined for each annular zone of the identified input image, as well as for the zone located around the largest ring of the cutting of the input image 1. Following an additional step, a reconstruction R of a reconstructed image IR is carried out from the corrected images IC of the stack of images, by selecting and extracting, in each corrected image, a zone and reproducing this zone in a reconstructed image: the zone of the corrected image corresponding either to the sharpest zone (figure 5), or the zone whose optical transfer function is that identified and used for the deconvolution making it possible to generate the corrected image in the stack P of images.

[0127] This zone selection step is repeated as many times as necessary for the reconstituted IR image to include all the attention positions of all the images in the image stack, the reconstituted image thus corresponding to said dynamic visualization of the input image. All of the steps of the methods described above can be executed by instructions executable by a computer program in a computer.

[0128] Figure 6 shows, schematically, an apparatus 2 according to the invention, which comprises an image processing device 3, also according to the invention.

[0129] The device 3 ensures the processing of an input image 1 which comes from a camera module 4, the input image 1 being representative of a scene 5 acquired by a sensor 6 of the camera module through an optical system 7 of the camera module 4.

[0130] The image processing device 3 is configured to: a) determine a corresponding optical transfer function (PSF) between a part of the scene 5 associated with the part of the input image 1, and b) deconvolve, by the determined optical transfer function (PSF), the input image 1 to obtain a corrected image IC (see figures 1 to 5), so as to obtain a plurality of corrected images IC which together form the stack P of images.

[0131] Figure 7 is a schematic representation of a non-limiting exemplary embodiment of a device according to the present invention.

[0132] The device 800 comprises an electronic device 2 comprising a camera module 4 and an image processing device 3, as shown in FIG. 6.

[0133] Figure 8a is a schematic representation of a non-limiting exemplary embodiment of an apparatus according to the present invention.

[0134] The apparatus 910 comprises means configured to implement the invention, and in particular any one of the methods described above.

[0135] The apparatus 910 of Figure 8a may comprise a device according to the invention, and in particular the device 800 of Figure 7.

[0136] In the example shown in Figure 8a, the device 910 is a smartphone, or a tablet, comprising the device 800 of Figure 7. In particular, the device 910 comprises a display screen equipped with a detection surface, for example capacitive, and at least one camera.

[0137] Figure 8b is a schematic representation of another non-limiting exemplary embodiment of an apparatus according to the present invention. The apparatus 920 of Figure 8b comprises means configured to implement the invention, and in particular any of the methods described above.

[0138] The apparatus 920 of Figure 8b may comprise a device according to the invention, and in particular the device 800 of Figure 7.

[0139] In the example shown in Figure 8b, the device 920 is a virtual reality, VR, headset, or an augmented reality headset, comprising the device 800 of Figure 7. In particular, the headset 920 comprises a display screen, a sensor for detecting the position aimed by an eye, or the eyes, of the user on said display screen.

[0140] In the example shown in Figure 8b, the headset 920 does not include imaging means for capturing images of the scene. In this case, the image(s) of the scene to be displayed by the headset 920 are provided by another device to said headset 920.

[0141] Alternatively, the headset 920 may comprise at least one camera for capturing images of the scene in which it is located to display them on its screen, optionally after enriching said images, for example in the context of an augmented reality application.

[0142] Figure 8c is a schematic representation of a non-limiting exemplary embodiment of an apparatus according to the present invention.

[0143] The apparatus 930 comprises means configured to implement the invention, and in particular any one of the methods described above.

[0144] The apparatus 930 may comprise a device according to the invention, and in particular the device 800 of FIG. 7.

[0145] In the example shown in Figure 8c, the apparatus is a medical imaging apparatus, such as an endoscope, an ultrasound apparatus, etc. comprising the device 800 of Figure 7. In particular, the medical imaging apparatus 930 comprises a display screen equipped with a detection surface, for example capacitive. The medical imaging apparatus further comprises an imaging means formed by a distal objective connected to an imaging module (not shown).

[0146] Figure 9 is a schematic representation of a non-limiting exemplary embodiment of a vehicle according to the present invention.

[0147] The vehicle 1000 of FIG. 9 comprises means configured to implement the invention, and in particular any one of the methods described above.

[0148] The vehicle 1000 may comprise a device according to the invention, and in particular the device 800 of FIG. 7.

[0149] In the example shown in Figure 9, the vehicle 1000 is a land vehicle, in particular a car, comprising the device 800. In particular, the vehicle 1000 comprises a display screen equipped with a detection surface, for example capacitive, arranged in the passenger compartment of the vehicle 1000. The vehicle 1000 further comprises at least one camera, for example arranged on the windshield of the vehicle 1000.

[0150] Of course, the invention is not limited to the examples which have just been described.

Claims

CLAIMS 1. Method for obtaining a stack (P) of images (IC) from an input image (1) depending on a raw image acquired by a sensor (6) of a camera module (4) through an optical system (7) of the camera module (4), said raw image being representative of a scene (5), the method being implemented by computer and being characterized in that it comprises, for each of a plurality of distinct parts of said input image (1), the following steps: a) determining a corresponding optical transfer function (PSF) between a part (Z) of the scene (7) associated with the part of the input image (1), b) deconvolution (D), by the determined optical transfer function, of the input image (1) to obtain a corrected image (IC), so as to obtain a plurality of corrected images (IC), said plurality of corrected images obtained forming said stack (P) of images.

2. Method according to claim 1, characterized in that step a) is carried out by determining, in said input image (1), a part (Z, zone) of the scene (5) where objects are all located substantially at the same distance (di) from the sensor (6).

3. Method according to claim 1, characterized in that step a) consists of producing a mesh of the input image (1) leading to image portions and associating with each portion an average optical transfer function between the corresponding part of the scene and the sensor (6) of the camera module (4).

4. Method according to any one of claims 1 to 3, characterized in that step b) is carried out by composition by the optical transfer function in the frequency domain.

5. Method according to any one of claims 1 to 3, characterized in that step b) is carried out by composition with a correction function resulting from the optical transfer function.

6. A method according to any one of claims 1 to 5, further comprising, for each of said plurality of corrected images forming said image stack: - cutting said corrected image into distinct parts of said input image which correspond to the parts of the scene of the identified optical transfer functions, - assigning a sharpness index (NET) to each distinct part of said input image by applying an image sharpness estimator, and - creation of a sharpness index map (CNET) associated with each of said plurality of corrected images (IC).

7. Method according to any one of claims 1 to 5, further comprising, for each of said plurality of corrected images (IC) forming said stack (P) of images: - cutting said corrected image (IC) into distinct parts of said input image acquired by said sensor which correspond to the parts of the scene of the identified optical transfer functions, - assigning a parameter representing a distance between said optical system and an object in each distinct part of said input image using the optical transfer function identified for the scene part corresponding to the distinct image part, and - creating said focal length map associated with each of said plurality of corrected images (IC).

8. The method of claim 6, further comprising the following steps: - receiving an attention position (x,y) in the input image (1), by choosing a distinct image part in said input image, - identification, in said stack of images, of the corrected image (IC) where the part of the scene corresponding to the position of attention (x, y) in the image is the sharpest, presenting a maximum value of said sharpness index, - selection in the identified corrected image (IC), of the attention position (x, y)NET, and positioning in a reconstituted image (IR), of the selected attention position (x, y)NET, said steps being repeated as many times as necessary so that the reconstituted image (IR) includes all the attention positions (x, y)NET of all the corrected images (IC) of the stack (P) of images, the reconstituted image (IR) thus corresponding to said dynamic visualization of the input image.

9. A computer program comprising executable instructions which, when executed by a computer, implement the steps of the method according to any one of claims 1 to 8.

10. Processing device (3) for an input image (1) from a camera module (4), the input image (1) being representative of a scene (5) acquired by a sensor (6) of the camera module (4) through an optical system (7) of the camera module (4), the processing device (3) being configured to: a) determine a corresponding optical transfer function (PSF) between a part of the scene (5) associated with the part of the input image (1), and b) perform the deconvolution (D), by the determined optical transfer function, of the input image (1) to obtain a corrected image (IC), so as to obtain a plurality of corrected images (IC), said plurality of corrected images (IC) obtained forming said stack (P) of images.

11. Electronic device (2) comprising a camera module (4) and an image processing device (3) according to claim 10.

Images