Method for acquiring image stack and computer program product implementing method

Abstract

The invention relates, inter alia, to a method for acquiring an image stack (P) from an input image (1) dependent on an original image acquired by a sensor (6) of a camera module (4) via an optical system (7) of the camera module (4), the original image representing a scene (5), the method being implemented by a computer, characterised in that the image stack (P) is acquired from the input image (1) by means of an optical system (7) of the camera module (4). For each of a plurality of different portions of the input image (1), the method comprises the steps of: a) determining a respective optical transfer function (PSF) between portions (Z) of the scene (7) associated with the portion of the input image (1), b) deconvolving (D) the input image (1) using the determined optical transfer functions to obtain a corrected image (IC), thereby obtaining a plurality of corrected images (IC), the plurality of corrected images obtained form the image stack (P).

Description

Technical Field

[0001] This invention relates to a method for obtaining an image stack from an input image (or an acquired image).

[0002] The present invention also relates to a computer program product for implementing the method, and an electronic device comprising the computer program product.

[0003] This invention applies to the field of digital imaging, particularly digital photography equipment and digital cameras, such as those used in mobile phones, tablets, or laptops.

[0004] The present invention can also be applied to images displayed in motor vehicles or flying equipment, for example, to display specific visual information to the driver or pilot, or to provide clearer images for autonomous driving or flight systems.

[0005] This invention can also be applied to the medical field, for example, to render images obtained from medical diagnostic observations.

[0006] This invention can also be used in video surveillance to render clearer images with greater depth of field. Background Technology

[0007] When acquiring an image, certain areas of the rendered image are sharper than others: the sharpness of a region in the acquired and rendered image is achieved by taking into account the distance when the image of the object was taken and adjusting the camera lens based on this distance; therefore, the sharpness of the area around the object in the acquired image is lower than the sharpness of the area corresponding to the lens setting.

[0008] To obtain an image where all areas are in focus, the focus conditions must be changed for each area that appears in the image: the result is a stack of images with different areas in focus; this is called a focus-shifted image stack.

[0009] This means taking as many images as there are objects in the image, adjusting the camera's focal length each time so that each object in the image is rendered in sharp focus in its dedicated image.

[0010] Another problem arises when the object is in motion: the scene being observed needs to be changed when the image is taken, and the focus conditions need to be changed over time intervals.

[0011] One of the objectives of this invention is to improve image rendering by eliminating certain aberrations in sharp areas.

[0012] Another object of the present invention is to provide a method that can present each object in sharp focus without acquiring multiple images (which is especially important in scenarios where objects are moving). Summary of the Invention

[0013] Therefore, according to a first aspect, the present invention relates to a method for obtaining an image stack from an input image that depends on a raw image acquired by a sensor of a camera module through the optical system of the camera module, the raw image representing a scene, the method being implemented by a computer, characterized in that, for each of a plurality of different portions of the input image, it includes the following steps:

[0014] a) Determine the corresponding optical transfer functions between parts of the scene associated with parts of the input image.

[0015] b) The input image is deconvolved using a defined optical transfer function to obtain the corrected image.

[0016] This results in the acquisition of multiple corrected images, which together form the image stack.

[0017] In this way, an image stack can be obtained, in which each corrected image includes a distinct and sharp portion of the scene represented by the input image (which is obtained from the original image, and if the original image is untransformed, the input image can correspond to the original image). In other words, from the input image representing the scene, multiple images can be obtained, each of which clearly and in focus presents at least a portion of the scene.

[0018] Advantageously, step a) is performed by determining a portion of the scene in the input image in which objects are all located at substantially the same distance from the sensor.

[0019] In addition, step a) includes meshing the input image into multiple image parts and associating the corresponding part of the scene with the average optical transfer function between the camera module sensor and each image part.

[0020] Preferably, step b) is performed by synthesis using an optical transfer function in the frequency domain.

[0021] According to an alternative embodiment, step b) is performed by synthesis using a correction function derived from the optical transfer function.

[0022] Advantageously, furthermore, for each of the plurality of corrected images forming the image stack, the method further includes the following step:

[0023] - Segment the corrected image into distinct images corresponding to the scene portion of the input image and the identified optical transfer function.

[0024] - By applying an image sharpness estimator, a sharpness index is assigned to each different part of the input image, and

[0025] - Create a sharpness index map associated with each of the plurality of corrected images.

[0026] According to an alternative embodiment, for each of the plurality of corrected images forming the image stack, the method further includes the following steps:

[0027] - The corrected image is segmented into different parts of the input image acquired by the sensor, corresponding to the scene portion of the identified optical transfer function.

[0028] - Using optical transfer functions identified for scene portions corresponding to different image parts, parameters representing the distance between the optical system and the object are assigned to each different portion of the input image, and

[0029] - Create a focal length map associated with each of the plurality of corrected images.

[0030] In a method that allows the creation of a sharpness index map associated with each of the plurality of corrected images, the steps described above may also be included:

[0031] - By selecting different portions of the input image, the location of interest in the image is received.

[0032] - Identify the corrected image in the image stack, in which the portion of the scene corresponding to the area of ​​interest in the image is the sharpest, and the sharpness index value is the largest.

[0033] - Select the location of interest in the identified corrected image and locate the selected location of interest in the reconstructed image.

[0034] The steps are repeated multiple times as needed, so that the reconstructed image includes all points of interest in all images in the image stack, and thus the reconstructed image corresponds to the dynamic display of the input image.

[0035] The present invention also relates to a computer program containing executable instructions that, when executed by a computer, implement the steps of the method as defined above.

[0036] The computer program can be written in any computer language, such as machine language, C, C++, JAVA, PYTHON, etc.

[0037] According to another aspect, the present invention relates to an image processing apparatus for processing an input image from a camera module, the input image representing a scene acquired by the sensor of the camera module through the optical system of the camera module, the processing apparatus being configured to:

[0038] a) Determine the corresponding optical transfer functions between parts of the scene associated with parts of the input image.

[0039] b) The input image is deconvolved using the determined optical transfer function to obtain the corrected image.

[0040] This results in multiple corrected images, which together form the image stack.

[0041] Advantageously, the present invention also relates to an electronic device comprising a camera module and an image processing apparatus as defined above.

[0042] Device

[0043] According to another aspect of the invention, an apparatus is proposed that includes means configured to implement all the steps of the method according to the invention.

[0044] The device according to the invention can be any type of device, or can be integrated into any type of device, such as a smartphone, tablet computer, computer, computing machine, processor, or computer chip, which is programmed, for example, to implement the method according to the invention by running a computer program according to the invention.

[0045] equipment

[0046] According to another aspect of the present invention, an apparatus is provided comprising:

[0047] - Image display device,

[0048] - At least one device for detecting the location of a target, and

[0049] - At least one computing device;

[0050] The device is configured to implement all the steps of the method according to the invention.

[0051] Devices with cameras

[0052] The device may not include an image acquisition device. In this case, the device is used to display one or more images acquired by another device.

[0053] Alternatively, the device may include an image acquisition means, such as a camera or camera module. In this case, the device can be used to display one or more images acquired by the device or another device.

[0054] smartphones or tablets

[0055] Specifically, the device can be a user device including a display screen, such as a smartphone or tablet. In this case, the detection device can be a touch-sensitive surface, or can include a touch-sensitive surface, particularly a touch-sensitive surface integrated into or associated with the display screen of the device.

[0056] Computerized User Equipment

[0057] Specifically, the device may be a user device including a display screen, such as a computer. In this case, the detection device may be a touch-sensitive surface, or may include a touch-sensitive surface, particularly a touch-sensitive surface integrated into or associated with the computer's display screen, or a pointer moved by, for example, a mouse or the computer's arrow keys.

[0058] TV equipment

[0059] Specifically, the device could be a television. In this case, the detection device could be a camera integrated into the television to detect the viewer's gaze and head position, or, for example, a pointer moved by the television's remote control.

[0060] Virtual Reality Headset

[0061] Specifically, the device can be a virtual reality headset or an augmented reality headset, which includes a display screen or a projector associated with a projection surface onto which each image is projected. In this case, the detection device can be a sensor or can include sensors mounted on the headset, particularly optical sensors.

[0062] Of course, the devices according to the present invention are not limited to the examples described above.

[0063] Medical imaging equipment

[0064] Specifically, the device can be a medical imaging device.

[0065] Specifically, the device can be an endoscope, an ultrasound machine, etc.

[0066] Transportation

[0067] According to another aspect of the present invention, a means of transportation is provided, comprising:

[0068] Image display device, and

[0069] At least one target position detection device,

[0070] At least one computing device;

[0071] It is configured to implement all the steps of the method according to the invention.

[0072] Vehicles with cameras

[0073] The vehicle may not include an image acquisition device. In this case, the scene images are provided by one or more other devices or other vehicles.

[0074] Alternatively, the vehicle may include an image acquisition device, such as a camera or camera module. In this case, the scene image is captured by the image acquisition device, or provided by another device or another vehicle.

[0075] Examples of vehicles

[0076] In some embodiments, the vehicle may be an autonomous land vehicle or a non-autonomous land vehicle, such as a car.

[0077] In some embodiments, the vehicle may be an autonomous or non-autonomous flying vehicle, such as a drone, an airplane, or a helicopter.

[0078] In some embodiments, the vehicle may be an autonomous or non-autonomous maritime vehicle, such as a boat or submarine.

[0079] Image type

[0080] 2D images

[0081] In some 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.

[0082] Where appropriate, an image stack includes at least one 2D image. In particular, each image in the image stack is a 2D image.

[0083] In the appropriate context, a clear image everywhere is a 2D image.

[0084] 3D images

[0085] In some 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.

[0086] Where appropriate, an image stack contains at least one 3D image. In particular, each image in the image stack is a 3D image.

[0087] In the right context, a clear image anywhere is a 3D image.

[0088] Brief description of the attached figures

[0089] The invention will be better understood by reading the following description, which is given only by way of non-limiting example and with reference to the accompanying drawings:

[0090] [ Figure 1 [ ] is a schematic diagram illustrating the steps involved in implementing the method of the first embodiment of the present invention.

[0091] [ Figure 2 [ ] is a schematic diagram illustrating the steps involved in implementing the method of the second embodiment of the present invention.

[0092] [ Figure 3 [ ] is a schematic diagram illustrating the steps involved in implementing the method of the third embodiment of the present invention.

[0093] [ Figure 4 The additional steps of the method according to the invention are illustrated schematically.

[0094] [ Figure 5 It shows Figure 4 Alternative embodiments of the illustrated embodiments, and

[0095] [ Figure 6 [Illustration of the device according to the invention located in front of the scene]

[0096] [ Figure 7 [Illustration] is a schematic diagram of a non-limiting embodiment of the device according to the present invention;

[0097] [ Figures 8a-8c ]: Figures 8a to 8c These are schematic diagrams of a non-limiting embodiment of the device according to the present invention; and

[0098] [ Figure 9 [Illustration] is a schematic diagram of a non-limiting embodiment of a vehicle according to the present invention.

[0099] It should be clearly understood that the embodiments described below are not limiting. In particular, variations of the invention may be envisioned to include only the features selected from the other disclosed features below—provided that the selected features are sufficient to provide a technical benefit or distinguish the invention from the prior art. This selection includes at least one preferred functional feature that does not contain structural details, or only contains a portion of structural details—provided that such partial structural details alone are sufficient to provide a technical benefit or distinguish the invention from the prior art.

[0100] In particular, all the described variations and embodiments can be combined with each other—provided that there are no technical obstacles to such combinations.

[0101] In the accompanying drawings and the remainder of the description, the same reference numerals are used for features common to several of the drawings. Detailed Implementation

[0102] Figure 1 The steps of the method according to the invention are illustrated schematically.

[0103] Consider a camera module, such as the camera module of a smartphone device, which is equipped with an optical system and a sensor for acquiring raw images.

[0104] For example, this is a raw image taken by a smartphone, showing the scene.

[0105] The original image can be modified or left unmodified by applying gain correction, black level, pixel level, etc., to obtain... Figure 1 The input image 1 is marked in the middle, which corresponds to the original image or the original image that has been corrected.

[0106] The scene shown in input image 1 is considered to comprise multiple parts Z1 to ZN: these may correspond to parts of the scene located at different distances from the camera module sensor.

[0107] Each part Z1 to ZN has its own Optical Transfer Function (PSF). For example... Figure 1 As shown, the first step of this method includes determining the optical transfer function of a portion Z in the input image 1: therefore, for N portions Z of the input image 1, N optical transfer functions will be identified; this corresponds to Figure 1 The attached figures are labeled PSF(Z1), PSF(Z2), ..., PSF(ZN).

[0108] Then, for the image portion Z1 that presents the optical transfer function PSF(Z1), a deconvolution D is performed on the input image 1 using the determined optical transfer function to obtain the corrected image IC1.

[0109] For the second image portion Z2 that presents the optical transfer function PSF(Z2), deconvolve D on the input image 1 using the determined optical transfer function to obtain the corrected image IC2.

[0110] For the third image portion Z3, and so on up to the Nth image portion ZN, the corrected images IC3 to ICN are obtained.

[0111] For a color input image RGB (red, green, blue), it should be understood that the image can be an image obtained directly from the sensor (i.e., the original image mentioned above), or an image obtained directly from each of the so-called Bayer matrix points R, G1, G2, B, or a so-called debayered image, i.e., an image that has been interpolated relative to the detection matrix points R, G1, G2, B.

[0112] The term PSF function will be used below to refer to the three optical transfer functions PSFR, PSFV, and PSFB, which respectively correlate the projection of each scene region to three sensor wavelength bands.

[0113] Furthermore, it should be noted that deconvolution D can be achieved through synthesis using optical transfer functions in the frequency domain, requiring minimal computation. This falls under the category of algorithmic cyclic deconvolution.

[0114] Alternatively, deconvolution D can be performed by synthesizing a correction function derived from the optical transfer function: for example, this technique could be of the Wiener filter type.

[0115] The result is to obtain N corrected images (or multiple corrected images) IC1 to ICN, which form a defined image stack P.

[0116] To determine the N parts of the input image 1 that require the application of the method according to the present invention, several schemes were employed:

[0117] In input image 1, it can be determined that the objects in the scene are all located at approximately the same distance d from the distance sensor: Figure 2 This option is illustrated schematically: on input image 1, the distance d of the object in the scene is estimated, and the optical transfer function (PSF) corresponding to the object at distance d is determined.

[0118] All objects located at the same first distance from the sensor are marked as objects located at distance d1, all objects located at the same second distance from the sensor are marked as objects located at distance d2, ... all objects located at the same i-th distance from the sensor are marked as objects located at distance di.

[0119] Figure 4 An embodiment is shown, in which, for example, five regions are identified in input image 1, each of which has a substantially uniform or uniform optical transfer function.

[0120] Using the method according to the present invention, a stack P containing five corrected images IC1 to IC5 is obtained.

[0121] For the corrected image IC1, deconvolution D considers the optical transfer function of the object located in region 1 of the scene. For the corrected image IC2, deconvolution D considers the optical transfer function of the object located in region 2 of the scene.

[0122] This process continues until five corrected images are obtained.

[0123] According to an optional embodiment, the first step (aimed at determining image portion Z) may include meshing the input image 1 into image portions and associating each image portion with a corresponding portion of the scene with the average optical transfer function between the sensor of the camera module.

[0124] Figure 5 An example of this embodiment is shown: the grid generated on input image 1 is regular. It should be understood that other grids can be generated without departing from the scope of the invention.

[0125] Figure 3 As schematically shown, the first part of the grid is considered as region 1 in the input image 1 at a distance d1 from the optical system sensor.

[0126] For each region 1, 2, ... K (K corresponds to the number of regions defined in the grid), the average optical transfer function PSF(di) of the considered region is identified, and the image is deconvolved to obtain the corrected image of the considered region 1. The number of corrected images in the image stack P is the same as the number of regions defined by the grid.

[0127] According to an advantageous aspect of the invention, the method may include steps other than the step of creating an image stack: for each corrected image IC forming the image stack, it is conceivable to divide it into multiple different portions corresponding to scene portions of an identified optical transfer function.

[0128] Then, using the optical transfer function identified for scene portions corresponding to different image parts, parameters representing the distance between the optical system and the object are assigned to each different portion of the input image.

[0129] Finally, a focal length map associated with each of the plurality of corrected images is created: this provides a depth map.

[0130] Figure 1 The method according to the invention may include additional steps, particularly creating a graph based on the sharpness index CNET of an image portion (from which a corrected image has been generated).

[0131] For each image in the stack P of the corrected image IC, the corrected image will be segmented into multiple distinct parts of the input image corresponding to the scene portion of the identified optical transfer function.

[0132] Then, by applying an image sharpness estimator, a sharpness index is assigned to each different part of the input image: this step corresponds to Figure 1 The attached figure is labeled NET.

[0133] Finally, a sharpness index graph CNET is created, which is associated with each of the plurality of corrected images IC1 to ICN.

[0134] like Figure 5 As shown, based on this sharpness map CNET, different image portions in the input image 1 are further received (e.g., ...). Figure 5 The point of interest (x,y)1 in the corrected image IC1, wherein the part of the scene corresponding to the point of interest (x,y)1 in the image is the sharpest (corresponding to the reference numeral (x,y)1 NET), and has the maximum value of the sharpness index.

[0135] Finally, a position of interest (x,y)1NET is selected from the identified corrected image IC1, and the selected position of interest (x,y)1 is located in the reconstructed image IR (the reference numeral R corresponds to the image reconstruction step). This step is repeated multiple times as needed, so that the reconstructed image IR includes all positions of interest (x,y)nNET of all corrected images IC1 in the image stack P, and thus the reconstructed image IR corresponds to the dynamic display of the input image.

[0136] In another application of the present invention, Figure 4 and Figure 5 It also demonstrates the possibility of reconstructing an image from a stack P of the corrected image IC.

[0137] Figure 4 An example is shown where concentric regions are marked on input image 1, wherein the optical transfer function is determined for each marked annular region of the input image and the region around the largest annular region of input image 1 after cropping.

[0138] In a further step, based on the corrected image IC in the image stack, reconstruction R of the reconstructed image IR is performed by selecting and extracting regions from each corrected image and reproducing those regions in the reconstructed image: the regions in the corrected images correspond to the clearest regions ( Figure 5 ), or the region corresponding to which its optical transfer function is identified and used for deconvolution (which is used to generate the corrected image in the image stack P).

[0139] This region selection step is repeated multiple times as needed, so that the reconstructed image IR includes all points of interest in all images in the image stack, and thus the reconstructed image corresponds to the dynamic display of the input image.

[0140] All steps in the above method can be executed by instructions from a computer program in a computer.

[0141] Figure 6The device 2 according to the invention is shown schematically, which includes an image processing device 3 also according to the invention.

[0142] Device 3 processes input image 1 from camera module 4, which represents scene 5 acquired by sensor 6 of camera module 4 through optical system 7 of camera module 4.

[0143] The image processing device 3 is configured as follows:

[0144] a) Determine the corresponding optical transfer function (PSF) between the parts of scene 5 associated with the part of input image 1, and

[0145] b) Deconvolve the input image 1 using the determined optical transfer function (PSF) to obtain the corrected image IC (see [link to image]). Figures 1 to 5 This yields multiple corrected images IC, which together form an image stack P.

[0146] Figure 7 This is a schematic diagram of a non-limiting embodiment of the device according to the present invention.

[0147] Device 800 includes electronic device 2, which includes, for example, Figure 6 The camera module 4 and image processing device 3 are shown.

[0148] Figure 8a This is a schematic diagram of a non-limiting embodiment of the device according to the present invention.

[0149] The device 910 includes means configured to implement the present invention, particularly any of the methods described above.

[0150] Figure 8a Device 910 may include means according to the invention, particularly Figure 7 Device 800.

[0151] exist Figure 8a In the example shown, device 910 includes Figure 7 The device 800 is a smartphone or tablet. In particular, the device 910 includes a display screen equipped with a sensing surface (e.g., a capacitive sensing surface) and at least one camera.

[0152] Figure 8b This is a schematic diagram of another non-limiting embodiment of the device according to the present invention.

[0153] Figure 8b The device 920 includes means configured to implement the present invention, particularly any of the methods described herein.

[0154] Figure 8bDevice 920 may include means according to the invention, particularly Figure 7 Device 800.

[0155] exist Figure 8b In the example shown, device 920 includes Figure 7 The device 800 is a virtual reality (VR) headset or augmented reality headset. In particular, the headset 920 includes a display screen and sensors for detecting the aiming position of a user's single or binoculars on the display screen.

[0156] exist Figure 8b In the example shown, the head-mounted device 920 does not include an imaging device for capturing scene images. In this case, the scene image to be displayed by the head-mounted device 920 is provided to the head-mounted device 920 by another device.

[0157] Alternatively, the head-mounted device 920 may include at least one camera to capture an image of its surrounding scene and display it on its screen (optionally after the image has been enhanced) as part of, for example, an augmented reality application.

[0158] Figure 8c This is a schematic diagram of a non-limiting embodiment of the device according to the present invention.

[0159] The device 930 includes means configured to implement the present invention, particularly any of the methods described herein.

[0160] Device 930 may include means according to the invention, in particular Figure 7 Device 800.

[0161] exist Figure 8c In the example shown, the device is Figure 7 The device 800 is a medical imaging apparatus, such as an endoscope, ultrasound equipment, etc. Specifically, the medical imaging apparatus 930 includes a display screen equipped with a sensing surface (e.g., a capacitive sensing surface). The medical imaging apparatus also includes an imaging device formed by a distal lens connected to an imaging module (not shown).

[0162] Figure 9 This is a schematic diagram of a non-limiting embodiment of a vehicle according to the present invention.

[0163] Figure 9 The vehicle 1000 shown includes means configured to implement the present invention, particularly any of the methods described herein.

[0164] The vehicle 1000 may include the device according to the invention, in particular Figure 7 Device 800.

[0165] exist Figure 9 In the example shown, vehicle 1000 is a land vehicle, particularly an automobile, that includes device 800. Specifically, vehicle 1000 includes a display screen equipped with a sensing surface (e.g., a capacitive sensing surface), which is disposed within the passenger compartment of vehicle 1000. Vehicle 1000 also includes at least one camera, for example, mounted on the windshield of vehicle 1000.

[0166] Of course, the present invention is not limited to the examples described above.

Claims

1. A method for acquiring an image (IC) stack (P) based on an input image (1), the input image depending on an original image acquired by a sensor (6) of a camera module (4) through an optical system (7) of the camera module (4), the original image representing a scene (5), the method being implemented by a computer, and characterized in that, for each of a plurality of different portions of the input image (1), the method comprises the following steps: a) Determine the corresponding optical transfer function (PSF) between the portion (Z) of the scene (7) associated with that portion of the input image (1), b) The input image (1) is deconvolved (D) using the determined optical transfer function to obtain the corrected image (IC). This results in the acquisition of multiple corrected images (ICs), which together form the image stack (P).

2. The method according to claim 1, characterized in that, Step a) is performed by determining a portion (Z, region) of the scene (5) in the input image (1), in which objects are located at substantially the same distance (di) from the sensor (6).

3. The method according to claim 1, characterized in that, Step a) includes meshing the input image (1) into image portions and associating the average optical transfer function between the corresponding portion of the scene and the sensor (6) of the camera module (4) with each image portion.

4. The method according to any one of claims 1 to 3, characterized in that, Step b) is performed by synthesizing the optical transfer function in the frequency domain.

5. The method according to any one of claims 1 to 3, characterized in that, Step b) is performed by synthesis using a correction function derived from the optical transfer function.

6. The method according to any one of claims 1 to 5, wherein for each of the plurality of corrected images forming the image stack, the method further comprises: The corrected image is segmented into different parts of the input image corresponding to the scene portion of the identified optical transfer function. By applying an image sharpness estimator, a sharpness index (NET) is assigned to each different portion of the input image, and Create a sharpness index map (CNET) associated with each of the plurality of corrected images (ICs).

7. The method according to any one of claims 1 to 5, wherein for each of the plurality of corrected images (IC) forming the image stack (P), the method further comprises: The corrected image (IC) is segmented into different parts of the input image acquired by the sensor, corresponding to the scene portion of the identified optical transfer function. Using the optical transfer function identified for different image portions of the scene, parameters representing the distance between the optical system and the object are assigned to each different portion of the input image, and Create the focal length map associated with each of the plurality of corrected images (ICs).

8. The method according to claim 6, further comprising the following step: The focus position (x, y) in the input image (1) is received by selecting different image portions in the input image. The corrected image (IC) is identified in the image stack, in which the portion of the scene corresponding to the location of interest (x, y) in the image is the sharpest, and the sharpness index value is the largest. Select the location of interest (x, y) NET in the identified corrected image (IC), and locate the selected location of interest (x, y) NET in the reconstructed image (IR). The steps are repeated multiple times as needed, so that the reconstructed image (IR) includes all points of interest (x, y) NET of all corrected images (IC) in the image stack (P), and thus the reconstructed image (IR) corresponds to the dynamic display 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. A processing apparatus (3) for processing an input image (1) from a camera module (4), the input image (1) representing 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 apparatus (3) being configured to: a) Determine the corresponding optical transfer function (PSF) between the portion of the scene (5) associated with that portion of the input image (1), and b) The input image (1) is deconvolved (D) using the determined optical transfer function to obtain the corrected image (IC). This results in the acquisition of multiple corrected images (ICs), which together form the image stack (P).

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

Images