WO2024135218A1 - 画像処理装置、画像処理方法およびプログラム - Google Patents

画像処理装置、画像処理方法およびプログラム Download PDF

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WO2024135218A1
WO2024135218A1 PCT/JP2023/042033 JP2023042033W WO2024135218A1 WO 2024135218 A1 WO2024135218 A1 WO 2024135218A1 JP 2023042033 W JP2023042033 W JP 2023042033W WO 2024135218 A1 WO2024135218 A1 WO 2024135218A1
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pixel
image
images
depth
value
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English (en)
French (fr)
Japanese (ja)
Inventor
拓磨 杉
隆輝 今村
深 臼杵
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Shizuoka University NUC
Hiroshima University NUC
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Shizuoka University NUC
Hiroshima University NUC
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Priority to JP2024565699A priority Critical patent/JPWO2024135218A1/ja
Priority to KR1020257019877A priority patent/KR20250112803A/ko
Priority to CN202380087561.7A priority patent/CN120380772A/zh
Priority to EP23906587.3A priority patent/EP4626014A4/en
Publication of WO2024135218A1 publication Critical patent/WO2024135218A1/ja
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G06T7/557—Depth or shape recovery from multiple images from light fields, e.g. from plenoptic cameras
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/95—Computational photography systems, e.g. light-field imaging systems
    • H04N23/957—Light-field or plenoptic cameras or camera modules
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/73—Deblurring; Sharpening
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10052—Images from lightfield camera
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro

Definitions

  • the present invention relates to an image processing device, an image processing method, and a program, and in particular to an image processing technique suitable for reconstructing an image refocused to an arbitrary depth from a light field image that records a light field.
  • High-speed, large-scale three-dimensional imaging is required to observe biomolecules and intracellular behavior in a delicate and dynamic manner.
  • One microscope that makes such three-dimensional imaging possible is the spinning disk confocal microscope.
  • the spinning disk confocal method a laser beam is shone onto a rotating disk containing an array of pinholes, creating multiple parallel beams of light, which are then used to rapidly scan the sample and form a confocal image. While this method can obtain high-resolution images, it requires scanning in the depth direction, which makes it time-consuming to measure, and it is not suitable for observing high-speed events.
  • the equipment tends to be large and expensive.
  • LFM Light Field Microscopy
  • a light field is a collection of light rays in three-dimensional space.
  • a microlens array in which many microlenses are arranged two-dimensionally, is placed on the intermediate image plane, and the light field can be recorded by obtaining the position where the light rays pass on the aperture stop of the objective lens and the position where the light rays are acquired on the image sensor. From the light field image recorded by LFM, a subaperture image equivalent to a small diameter lens can be obtained.
  • a subaperture image is a partial image of a subject captured by shifting the viewpoint, and each subaperture image has a deep depth of field due to the pinhole effect, and at the same time, it has parallax between other subaperture images. Therefore, an image refocused to any depth can be reconstructed by shifting and overlapping the subaperture images.
  • LFM is suitable for observing high-speed events because it can capture three-dimensional images at high speed in a single shot without scanning in the depth direction.
  • the resolution is significantly lower than that of the spinning disk confocal method. Therefore, in LFM, it is important to improve the spatial resolution.
  • the resolution has been improved by deconvolution processing of the light field image (see, for example, Non-Patent Document 1), or an incoherent multiscale scattering model has been introduced to perform computational optical sectioning (see, for example, Non-Patent Document 2).
  • the inventors of the present application disclosed an invention that enables improvement of the resolution in the XY directions of an LFM reconstructed image.
  • the objective of the present invention is to improve the resolution in the Z direction, i.e., the depth direction, of an LFM reconstructed image.
  • an image processing device that reconstructs an image refocused to an arbitrary depth from multi-viewpoint image data that is a collection of multiple sub-images captured of the same subject from multiple mutually shifted viewpoints
  • the image processing device including: an element image group generating unit that automatically generates an element image group by extracting element images that are captured from mutually different viewpoints from the multi-viewpoint image data; and a pixel profile generating unit that automatically generates a pixel profile that represents the relationship between the shift amount of the element image and the pixel value of each pixel by partially overlapping element images related to adjacent viewpoints in the element image group by changing the shift amount, and setting the pixel value of each pixel to a value that reflects the pixel value of the pixel that overlaps between the element images.
  • An image processing device and a corresponding image processing method include a pixel depth determination unit that, for each pixel of each element image in the element image group, refers to the pixel profile and automatically associates with the pixel a depth corresponding to the amount of shift of the element image when the pixel value of the pixel takes an extreme value, and an image reconstruction unit that shifts and overlays the element images in the element image group by a shift amount corresponding to a specified depth, sets the pixel values of pixels not associated with the specified depth to essentially zero, and automatically reconstructs an image refocused to the specified depth.
  • the present invention can improve the depth resolution of, for example, a reconstructed image from a light field microscope.
  • FIG. 1 is a schematic diagram of an optical system including an image processing device according to an embodiment of the present invention
  • 1 is a flowchart of an image processing method according to an embodiment of the present invention.
  • 1A is a diagram showing an example of multi-view image data, (b) a group of element images, and (c) a reconstructed image.
  • FIG. 2 is a diagram illustrating a geometric relationship between parameters of an optical system according to an example.
  • 11A and 11B are diagrams for explaining overlay of element images in generating a pixel profile.
  • FIG. 13 is a diagram illustrating pixel depth linking according to an example.
  • 11A and 11B are diagrams comparing the resolutions of reconstructed images obtained by the simple average method, the weighted average method, and the image processing device according to the present embodiment.
  • ⁇ Embodiment> 1 is a schematic diagram of an optical system including an image processing device according to an embodiment of the present invention.
  • the optical system 100 includes an optical system 10, a storage device 20, and an image processing device 30.
  • the storage device 20 may be located on a cloud.
  • all or a part of the components of the image processing device 30 may also be located on a cloud.
  • the optical system 10 includes a main lens 1, a microlens array 2, and an image sensor 3.
  • the main lens 1 may include an objective lens and an imaging lens, but for convenience, the lens including these will be referred to as the main lens 1.
  • the microlens array 2 is configured by arranging multiple microlenses (lenslets) 2a two-dimensionally, and is disposed between the main lens 1 and the image sensor 3.
  • the image sensor 3 is a CCD sensor or CMOS sensor, etc., configured by arranging multiple light receiving elements (not shown) two-dimensionally, which perform photoelectric conversion of input light and output electrical signals. The electrical signals output from each light receiving element are converted into digital data by an A/D converter (not shown), and a light field image is output from the optical system 10 as multi-viewpoint image data.
  • the storage device 20 is a collection of storage devices such as RAM, ROM, SSD, and HDD.
  • the RAM is mainly used as a working memory when the image processing device 30 performs image processing.
  • the ROM, SSD, and HDD mainly store computer programs for operating the image processing device 30 or computer programs stored and provided on computer-readable, non-transitory recording media, multi-viewpoint image data output from the optical system 10, element images generated and used in the image processing described below, PQ arrays, pixel profiles, pixel depth tables, reconstructed images, and the like for temporary or long-term storage.
  • the image processing device 30 comprises an element image group generation unit 31, a pixel profile generation unit 32, a pixel depth determination unit 33, and a pixel reconstruction unit 34.
  • Each of these components can be realized as hardware such as an ASIC or FPGA, or as software in which a computer program stored in a recording medium (not shown) or in the storage device 20 is executed by a processor (not shown) such as a CPU or GPU. It can also be realized by appropriately combining hardware and software. While performing image processing, the image processing device 30 appropriately accesses the various storage devices that make up the storage device 20 to write and read data.
  • FIG. 2 is a flowchart of an image processing method according to one embodiment of the present invention. Each step is executed by the above-mentioned components of the image processing device 30.
  • the element image group generating unit 31 automatically generates an element image group by cutting out element images, which are images captured from different viewpoints, from the multi-viewpoint image data (S1).
  • FIG. 3 shows an example of multi-viewpoint image data, element images, and a reconstructed image.
  • the multi-viewpoint image data is captured by placing a sheet with the characters "/5" in a position shifted from the focal plane of the optical system 10.
  • the multi-viewpoint image data is a microlens image group in which a large number of microlens images (corresponding to sub-aperture images) MI captured by each microlens 2a of the microlens array 2 of the optical system 10 are arranged in a square lattice shape.
  • each microlens image MI is approximately circular, reflecting the circular shape of each microlens 2a.
  • the multi-viewpoint image data is a collection of multiple sub-images (microlens images MI in the example of FIG. 3) captured of the same subject from multiple viewpoints that are shifted from each other.
  • the element image group generator 31 cuts out, from each roughly circular microlens image MI, for example, a square area inscribed in the circle as an element image EI.
  • These element images EI are arranged in a square lattice pattern to form the element image group (b).
  • the cut-out element images are stored in the storage device 20.
  • a microlens image in which the character "/5" is partially captured by shifting the viewpoint by each microlens 2a, that is, a microlens image having parallax is recorded.
  • the light beam emitted from that part of the subject is recorded with a distance ⁇ between adjacent microlens images.
  • the distance ⁇ between the microlens images is ⁇ EI between the elemental images.
  • ⁇ EI ⁇ -(MLP-EI L )...(1)
  • a reconstructed image (c) is obtained by superimposing adjacent elemental images in the elemental image group while shifting them by a shift amount according to the parallax.
  • a reconstructed image (c) refocused on the position of the sheet on which the character "/5" is written is obtained by superimposing adjacent elemental images while shifting them by ⁇ EI.
  • an image refocused at an arbitrary depth position based on ray tracing in the reverse direction from the acquired image can be reconstructed.
  • FIG. 4 is a diagram showing a schematic diagram of the geometric relationship of each parameter of the optical system 10 according to an example.
  • O is an object at a position of depth d obj
  • A is an image forming point of the object O by the main lens 1
  • B and C are centers of adjacent microlenses 2a
  • B' and C' are the positions of light rays from the object O captured by the image sensor 3 via B and C.
  • ⁇ MLP(1-b/(d obj M 2 -a))...(2)
  • MLP is the pitch of the microlens 2a
  • M is the magnification of the objective lens of the main lens 1
  • a is the distance from the image-side focal plane T-NIP (Native Image Plane) of the imaging lens of the main lens 1 to the center of the microlens 2a
  • b is the distance from the center of the microlens 2a to the image sensor 3.
  • a is considered to be a positive value in order to show the geometric relationship, but if the direction from the object side to the image side is positive, a needs to be considered to be a negative value. For this reason, in formula (2), a is finally replaced with -a.
  • ⁇ EI the distance between rays originating from the same point between adjacent element images
  • the depth d obj of that point can be specified.
  • This ⁇ EI can be estimated from the change characteristics of the pixel value of each pixel when adjacent element images are partially overlapped by changing the shift amount. The specific procedure for estimating ⁇ EI will be described below.
  • the pixel profile generator 32 partially overlaps element images relating to adjacent viewpoints in the element image group by changing the amount of shift, and for pixels that overlap between the element images, sets values reflecting the pixel values of those pixels as the pixel values of those pixels, and automatically generates a pixel profile that represents the relationship between the amount of shift of the element images and the pixel values of each pixel (S2).
  • the generated pixel profile is stored in the storage device 20.
  • FIG. 5 is a diagram explaining the overlapping of element images in pixel profile generation. Overlapping pixels are shown with a crosshatch, the left column shows the overlapping state of the element images, and the right column shows the overlapping state of the element images expanded.
  • the element image group consists of four element images D, E, F, and G, two in each row, and each element image is five pixels in size.
  • the element images are arranged such that element image E is to the right of element image D, element image F is below element image D, and element image G is below element image E, i.e., to the right of element image F.
  • consecutive numbers starting from 1 are assigned to the rows and columns.
  • pixels belonging to element image D are referenced in the range from the first row to the fifth row and the first column to the fifth column
  • pixels belonging to element image G are referenced in the range from the sixth row to the tenth row and the sixth column to the tenth column.
  • pixel [m, n] refers to the pixel located in the mth row and nth column in the element image group.
  • Z is a depth parameter representing d obj .
  • d obj and the shift amount of the element image are represented by the same Z value.
  • d obj n, which indicates that the shift amount of the element image is n pixels.
  • the pixel profile generator 32 shifts adjacent element images by one pixel and overlaps them, calculates the pixel values of the overlapping pixels, and records them in the pixel profile. It is easy to determine which pixels in the element image group should be overlapped by referring to the PQ array that indicates how the pixels are overlapped.
  • This PQ array means that the pixel in the fifth row (fifth column) and the pixel in the sixth row (sixth column) overlap.
  • the pixel in the fifth row which is part of element images D and E
  • the pixel in the sixth row which is part of element images F and G
  • the pixel in the fifth column which is part of element images D and F
  • the pixel in the sixth column which is part of element images E and G.
  • values reflecting the pixel values of those pixels for example, average values or weighted sum values, are recorded in the pixel profile as the pixel values of those pixels.
  • the pixel values of overlapping pixels [5,10] and [6,10] are averaged, and the average value is recorded in the pixel profiles of both pixels.
  • the average of the pixel values of these four pixels is recorded in the pixel profiles of these pixels.
  • the pixel profile generator 32 shifts adjacent element images by two pixels, overlays them, calculates the pixel values of the overlapping pixels, and records them in the pixel profile.
  • the PQ array means that the pixels in the 4th and 5th rows (4th and 5th columns) overlap with the pixels in the 6th and 7th rows (6th and 7th columns).
  • values reflecting the pixel values of these pixels for example, average values or weighted sum values, are recorded in the pixel profile as the pixel values of these pixels.
  • the pixel values of the overlapping pixels [4, 10] and [6, 10] are averaged, and the average value is recorded in the pixel profiles of both pixels.
  • the average pixel values of these four pixels are recorded in the pixel profile of these pixels.
  • the PQ array will be made up of three or more rows.
  • a common PQ array can be used to overlap element images in the row direction and in the column direction, but if the matrix of element images has a different number of pixels, a PQ array for the row direction and a PQ array for the column direction must be prepared.
  • the pixel depth determination unit 33 refers to the pixel profile for each pixel of each element image in the element image group, and automatically associates the pixel with a depth corresponding to the shift amount of the element image when the pixel value of the pixel is at an extreme value (S3).
  • This association information is stored in the storage device 20 as a pixel depth table.
  • FIG. 6 is a diagram for explaining pixel depth linking according to an example.
  • the graph in the figure visualizes the pixel profile of the target pixel, with the vertical axis being the pixel value and the horizontal axis being the depth (Z value).
  • the target pixel is the central pixel of the image of the object captured in the four element images shown in the figure.
  • the image of the object is an approximately circular image that is bright at the center and dark toward the periphery. The larger the pixel value, the brighter the pixel is.
  • the pixel profile is recorded with an average value as the pixel value of overlapping pixels.
  • the pixel profile shown in FIG. 6 is merely an example, and not all pixel profiles will necessarily have the characteristics shown in FIG. 6.
  • the pixel profile may vary depending on the type and condition of the subject. For example, in an image containing two objects, one light and one dark, with different depths, a pixel profile may be generated in which the pixel values of the darker image are averaged with the pixel values of the brighter image, resulting in a pixel profile that exceeds the initial value.
  • the pixel depth determination unit 33 may refer to the pixel profile for each pixel of each element image in the element image group, and associate the pixel with a depth corresponding to the amount of shift of the element image when the pixel value of the pixel is within a predetermined range from the extreme value.
  • the pixel reconstruction unit 34 shifts and overlaps the element images in the element image group by a shift amount corresponding to the specified depth, and automatically reconstructs an image refocused to the specified depth by making the pixel values of pixels not associated with the specified depth substantially zero (S4).
  • a reconstructed image refocused to the depth corresponding to ⁇ EI is obtained by shifting and overlapping adjacent element images by ⁇ EI according to the parallax.
  • the pixel reconstruction unit 34 refers to the pixel depth table stored in the storage device 20, uses the pixels corresponding to the specified depth as they are, and makes the pixel values of the other pixels zero or almost zero, i.e., completely black or almost black, to automatically reconstruct the image.
  • the center pixel of the above object is used as it is, but when a range other than that is specified, the pixel value of the pixel is made zero or almost zero to reconstruct the image.
  • the image processing device 30 can perform computational optical sectioning to separate light rays from light sources at different depths from each other on a pixel-by-pixel basis, thereby increasing the Z-resolution of the reconstructed image. Furthermore, the image processing can be performed at the frame rate of the optical system 10, enabling three-dimensional imaging of high-speed events.
  • Figure 7 is a diagram comparing the resolution of reconstructed images by the simple average method, the weighted average method, and the image processing device 30.
  • the simple average method uses the average of the pixel values of overlapping pixels when element images are superimposed to reconstruct an image as the pixel value of the reconstructed image.
  • the weighted average method uses the weighted average of overlapping pixels when element images are superimposed to reconstruct an image, with the weights relatively large for pixels near the center of the element image area and relatively small for pixels at the edge, and uses the weighted average as the pixel value of the reconstructed image.
  • each algorithm shows a reconstructed image (X-Y diagram) at a certain depth reconstructed from light field image data capturing fluorescent particles, a depth image (X-Z diagram) viewed in the depth direction (Z direction) of the dashed line part in the X-Y diagram, a graph of depth vs. brightness of the dotted line part in the X-Y diagram, and a graph of depth vs. brightness of the dotted line part in the X-Z diagram.
  • the decimal values in each graph are the resolution (full width at half maximum).
  • the images reconstructed using the simple average method and the weighted average method are blurred in both the X and Z directions.
  • the X-direction resolution of the image reconstructed using the simple average method is 2.6537 ⁇ m
  • the Z-direction resolution is 5.3285 ⁇ m
  • the X-direction resolution of the image reconstructed using the weighted average method is 2.2468 ⁇ m
  • the Z-direction resolution is 6.6448 ⁇ m
  • the resolution of both is almost the same.
  • the X-direction resolution of the image reconstructed using the optical sectioning method of the image processing device 30 is 0.9074 ⁇ m
  • the Z-direction resolution is 1.5050 ⁇ m
  • the resolution is significantly improved not only in the X direction but also in the Z direction compared to the simple average method and the weighted average method.
  • the sub-images do not need to be arranged two-dimensionally, but can be arranged one-dimensionally, either vertically or horizontally.
  • Multi-viewpoint image data is not limited to light field images, but may be any collection of multiple sub-images of the same subject captured from multiple, mutually offset viewpoints. For example, it may be captured by a multi-camera system with multiple cameras lined up. Also, images captured by each camera in a multi-camera system do not need to be compiled into a single image data set; images captured by each camera may be saved in individual files. In this case, the set of image files is the multi-viewpoint image data.
  • the image processing device is capable of performing high-speed, high-resolution three-dimensional imaging, making it useful for light-field microscopes that observe high-speed events such as biomolecules and intracellular behavior.
  • Image processing device 31 Element image group generating unit 32 Pixel profile generating unit 33 Pixel depth determining unit 34 Image reconstructing unit

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CN202380087561.7A CN120380772A (zh) 2022-12-19 2023-11-22 图像处理装置、图像处理方法以及程序
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