Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 8-9, 11-14 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lu (Color Filter Array Demosaicking: New Method and Performance Measures, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 10, OCTOBER 2003).
Regarding claim 1, Lu teaches a method of applying upsampling to input pixels representing an image region to determine a block of upsampled pixels (To interpolate a missing value of one color plane, we first obtain its estimates from four different interpolation directions by exploiting the spectral correlation and then combine these estimates with proper weights. Page 1195 left column 3rd paragraph), wherein the input pixels have locations corresponding to a repeating quincunx arrangement of upsampled pixel locations (
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), the method comprising: analysing the input pixels to determine one or more weighting parameters (Therefore, to interpolate one missing color value, we combine the estimates from its four interpolation directions by assigning them with weights that measure the spatial correlations among the neighboring pixels along the corresponding interpolation directions. Page 1196 left column 2nd paragraph), the one or more weighting parameters being indicative of a directionality of filtering to be applied (To properly combine the estimates from the four interpolation directions, we assign each estimate with an appropriate weight, which is the reciprocal of the gradient magnitude along the respective interpolation direction. Page 1196 right column 5th paragraph) when upsampling is applied to the input pixels within the image region (We start with interpolating the green plane because it contains twice as many samples as the red or blue plane. Page 1196 left column 4th paragraph); and determining one or more of the upsampled pixels of the block of upsampled pixels in accordance with the directionality of filtering indicated by the determined one or more weighting parameters (
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. Page 1196 left column last paragraph.
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Regarding claim 2, Lu teaches the method of claim 1, wherein the one or more weighting parameters are indicative of relative horizontal and vertical variation (To properly combine the estimates from the four interpolation directions, we assign each estimate with an appropriate weight, which is the reciprocal of the gradient magnitude along the respective interpolation direction. Page 1196 right column 5th paragraph.
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) of the input pixels within the image region (We start with interpolating the green plane because it contains twice as many samples as the red or blue plane. Page 1196 left column 4th paragraph), and wherein the one or more of the upsampled pixels of the block of upsampled pixels are determined in accordance with the relative horizontal and vertical variation (To properly combine the estimates from the four interpolation directions, we assign each estimate with an appropriate weight, which is the reciprocal of the gradient magnitude along the respective interpolation direction. Page 1196 right column 5th paragraph) of the input pixels within the image region indicated by the determined one or more weighting parameters (
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. Page 1196 left column last paragraph.
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Regarding claim 3, Lu teaches the method of claim 1, wherein determining each of said one or more of the upsampled pixels comprises applying one or more kernels to at least some of the input pixels (Let us illustrate how the gradient magnitude is computed by using the top interpolation direction of as an example. Referring to Fig. 4, the gradient magnitude along the top interpolation direction is computed by applying a Sobel operator and a 1-D gradient operator on the Group A and Group B pixels, respectively. Page 1196 right column last paragraph.
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) in accordance with the determined one or more weighting parameters (
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. Page 1196 left column last paragraph).
Regarding claim 4, Lu teaches the method of claim 1, wherein each of said one or more of the upsampled pixels is at a respective upsampled pixel location which does not correspond with the location of any of the input pixels (
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Regarding claim 8, Lu teaches the method of claim 1, wherein determining each of said one or more of the upsampled pixels comprises, in accordance with the determined one or more weighting parameters (Since a large gradient magnitude implies low spatial correlation among pixels along the respective interpolation direction, proper weights of the four color-adjusted green values are given by (7), shown at the bottom of the page, where 1 in the denominator is included to avoid division by zero. Page 1196 right column last paragraph): applying one or more first kernels to at least a first subset of the input pixels to determine a horizontal component (Let us illustrate how the gradient magnitude is computed by using the top interpolation direction of as an example. Referring to Fig. 4, the gradient magnitude along the top interpolation direction is computed by applying a Sobel operator and a 1-D gradient operator on the Group A and Group B pixels, respectively. Page 1196 right column last paragraph.
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. It is common knowledge that similar procedure can be applied to determine a horizontal component.), applying one or more second kernels to at least a second subset of the input pixels to determine a vertical component (Let us illustrate how the gradient magnitude is computed by using the top interpolation direction of as an example. Referring to Fig. 4, the gradient magnitude along the top interpolation direction is computed by applying a Sobel operator and a 1-D gradient operator on the Group A and Group B pixels, respectively. Page 1196 right column last paragraph.
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), and combining the determined horizontal and vertical components (
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. Page 1196 left column last), such that each of said one or more of the upsampled pixels is determined in accordance with the directionality of filtering indicated by the determined one or more weighting parameters (
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. Page 1196 left column last paragraph.
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Regarding claim 9, Lu teaches the method of claim 8, wherein said combining the determined horizontal and vertical components comprises: multiplying the horizontal component by a first of the weighting parameters, a (
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), to determine a weighted horizontal component (
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. Page 1196 left column last paragraph); multiplying the vertical component by a second of the weighting parameters, b (
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), to determine a weighted vertical component (
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. Page 1196 left column last paragraph); and summing the weighted horizontal component and the weighted vertical component (
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. Page 1196 left column last paragraph).
Regarding claim 11, Lu teaches the method of claim 1, wherein said analysing the input pixels to determine one or more weighting parameters comprises: determining indications of image gradients for the image region based on the input pixels (
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); and using the determined indications of image gradients to determine the one or more weighting parameters (
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Regarding claim 12, Lu teaches the method of claim 11, wherein said determining indications of image gradients for the image region comprises: applying a first gradient kernel to the input pixels to determine an indication of an image gradient in a horizontal direction, dx (Let us illustrate how the gradient magnitude is computed by using the top interpolation direction of as an example. Referring to Fig. 4, the gradient magnitude along the top interpolation direction is computed by applying a Sobel operator and a 1-D gradient operator on the Group A and Group B pixels, respectively. Page 1196 right column last paragraph.
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. It is common knowledge that similar procedure can be applied to determine an indication of an image gradient in a horizontal direction.); and applying a second gradient kernel to the input pixels to determine an indication of an image gradient in a vertical direction, dy (Let us illustrate how the gradient magnitude is computed by using the top interpolation direction of as an example. Referring to Fig. 4, the gradient magnitude along the top interpolation direction is computed by applying a Sobel operator and a 1-D gradient operator on the Group A and Group B pixels, respectively. Page 1196 right column last paragraph.
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Regarding claim 13, Lu teaches the method of claim 12, wherein said using the determined indications of image gradients to determine the one or more weighting parameters comprises: determining a first weighting parameter, a, as a = clip(m(|dy| - |dx|) + 0.5,0,1), where m is a tuneable parameter (It is common knowledge that a weighting parameter for image gradient anisotropy can be defined as the difference between the gradients along two orthogonal directions, which reads on “(|dy| - |dx|)”, with m being a scaling factor and the clip function limiting the value to be between 0 and 1. Adding 0.5 to m(|dy| - |dx|) is to make a=b=0.5 when the gradient is isotropic, which is also common knowledge.); and determining a second weighting parameter, b, as b = 1 – a (It is common knowledge this can be a weighting parameter for image gradient anisotropy.).
Regarding claim 14, Lu teaches the method of claim 8, wherein the upsampled pixels in the block of upsampled pixels are non-sharpened upsampled pixels (In our view, since subjective image preference could vary from individual to individual, the images should be first reproduced as accurately as possible before any enhancement process, such as image sharpening, is applied. Page 1201 left column 2nd paragraph), wherein: said applying one or more first kernels to at least a first subset of the input pixels comprises applying a single first kernel to input pixels horizontally adjacent to the upsampled pixel being determined (Let us illustrate how the gradient magnitude is computed by using the top interpolation direction of as an example. Referring to Fig. 4, the gradient magnitude along the top interpolation direction is computed by applying a Sobel operator and a 1-D gradient operator on the Group A and Group B pixels, respectively. Page 1196 right column last paragraph.
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. It is common knowledge that similar procedure can be applied along the horizontal direction.); and said applying one or more second kernels to at least a second subset of the input pixels comprises applying a single second kernel to input pixels vertically adjacent to the upsampled pixel being determined (Let us illustrate how the gradient magnitude is computed by using the top interpolation direction of as an example. Referring to Fig. 4, the gradient magnitude along the top interpolation direction is computed by applying a Sobel operator and a 1-D gradient operator on the Group A and Group B pixels, respectively. Page 1196 right column last paragraph.
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Apparatus claim 17 is drawn to the apparatus corresponding to the method of using same as claimed in claim 1. Therefore apparatus claim 17 corresponds to method claims 1, and is rejected for the same reasons of anticipation as used above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Lu (Color Filter Array Demosaicking: New Method and Performance Measures, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 10, OCTOBER 2003) hereinafter Lu, in view of Leontaris (US Patent Pub. No.: US 2013/0113884 A1), hereinafter Leontaris.
Regarding claim 5, Lu teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Leontaris teaches wherein the input pixels are represented in two input blocks, wherein one of the two input blocks comprises the input pixels having locations corresponding to locations within odd rows of the repeating quincunx arrangement of upsampled pixel locations, and the other of the two input blocks comprises the input pixels having locations corresponding to locations within even rows of the repeating quincunx arrangement of upsampled pixel locations (
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It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lu to incorporate the teachings of Leontaris to represent the input pixels in two input blocks, one comprising the input pixels having locations corresponding to locations within odd rows of the repeating quincunx arrangement of upsampled pixel locations, and the other comprising the input pixels having locations corresponding to locations within even rows of the repeating quincunx arrangement of upsampled pixel locations in order to arrange the odd row and even row input pixels to be packed separately and not interleaved.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Lu (Color Filter Array Demosaicking: New Method and Performance Measures, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 10, OCTOBER 2003) hereinafter Lu, in view of Leontaris (US Patent Pub. No.: US 2013/0113884 A1), hereinafter Leontaris, further in view of Panteleev (US Patent Pub. No.: US 2023/0138718 A1), hereinafter Panteleev.
Regarding claim 6, Lu and Leontaris teach all of the elements of the claimed invention as stated in claim 5 except for the following limitations as further recited. However, Panteleev teaches wherein the image region is part of a current frame within a sequence of frames (In this example process, a current image in a sequence of images is rendered 502. [0069]), wherein the input pixels of a first of the input blocks (The image data (or image space) can be divided 504 into a plurality of tiles, such as groups of 3x3 pixels, and a single lighting sample selected for each tile to be compared to a corresponding sample of a prior image in this sequence. [0069]) are rendered for the current frame (For example, a denoiser might decide for a given pixel location to use the confidence signal to reject the previous frame information where confidence is low and keep the previous frame information where the confidence is high. [0053]. In this example process, a current image in a sequence of images is rendered 502. [0069]), and wherein the input pixels of a second of the input blocks (The image data (or image space) can be divided 504 into a plurality of tiles, such as groups of 3x3 pixels, and a single lighting sample selected for each tile to be compared to a corresponding sample of a prior image in this sequence. [0069]) are determined (For example, a denoiser might decide for a given pixel location to use the confidence signal to reject the previous frame information where confidence is low and keep the previous frame information where the confidence is high. [0053]) by performing temporal resampling on pixels of a previous frame (With this assumption, temporal resampling can skip tracing the visibility ray if the selected sample comes from the previous frame. [0068]) using motion vectors (In an example implementation, a compute pass can be performed that examines every pixel in a current frame and its associated motion vector, and finds a matching pixel in the previous frame. [0056]), and wherein pixels for the previous frame corresponding to the pixel locations of the input pixels of the second input block were rendered for the previous frame (Systems and methods described relate to the generation of image content. In order to provide for smoothing between sequential images, but avoid introducing lag into lighting effects, light information can be compared for regions between consecutive rendered frames. Abstract).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lu and Leontaris to incorporate the teachings of Panteleev to render the input pixels of a first of the input blocks for the current frame wherein the image region is part of a current frame within a sequence of frames and determine the input pixels of a second of the input blocks by performing temporal resampling on pixels of a previous frame using motion vectors wherein pixels for the previous frame corresponding to the pixel locations of the input pixels of the second input block were rendered for the previous frame in order to improve rendering quality.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lu (Color Filter Array Demosaicking: New Method and Performance Measures, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 10, OCTOBER 2003) hereinafter Lu, in view of Panteleev (US Patent Pub. No.: US 2023/0138718 A1), hereinafter Panteleev.
Regarding claim 7, Lu teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Panteleev teaches wherein all of the input pixels are rendered for the image region (In this example process, a current image in a sequence of images is rendered 502. [0069]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lu to incorporate the teachings of Panteleev to render all of the input pixels for the image region in order to improve rendering quality.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lu (Color Filter Array Demosaicking: New Method and Performance Measures, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 10, OCTOBER 2003) hereinafter Lu, in view of Janis (US Patent Pub. No.: US 12,749,146 B2), hereinafter Janis.
Regarding claim 10, Lu teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Janis teaches wherein said analysing the input pixels to determine one or more weighting parameters comprises processing the input pixels with an implementation of a neural network (In at least one embodiment, this upscaled image 210 can be provided as input to a neural network 212 to determine one or more blending factors or blending weights. Column 8 line 39), wherein the neural network has been trained (In at least one embodiment, this neural network can be trained at full resolution or reduced resolution, but at inference time can execute at reduced resolution. Column 13 line 25) to output an indication of the one or more weighting parameters to be indicative of the directionality of filtering to be applied (In at least one embodiment, a set of per-pixel weighting factors and anisotropic reconstruction kernel parameters ( e.g., elongated anisotropic Gaussian filters) can be received 408 from this neural network, where anisotropic parameters may be inferred for features such as edges detected in input image data. Column 13 line 26) when upsampling is applied to the input pixels within the image region (In at least one embodiment, an upsampling process can perform blending by upsampling an input color with one or more predicted anisotropic kernels, such as discussed previously. Column 6 line 6).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lu to incorporate the teachings of Janis to process the input pixels with an implementation of a trained neural network to output an indication of the weighting parameters to be indicative of the directionality of filtering to be applied when upsampling is applied to the input pixels in order to improve upsampling quality.
Claims 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lu (Color Filter Array Demosaicking: New Method and Performance Measures, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 10, OCTOBER 2003) hereinafter Lu, in view of Song (European Patent Publication number: EP0398861 A2), hereinafter Song.
Regarding claim 16, Lu teaches all of the elements of the claimed invention as stated in claim 14 except for the following limitations as further recited. However, Song teaches a method of applying adaptive sharpening to input pixels representing an image region to determine a block of output pixels (Method and apparatus for sharpening edges and details of a digital image without amplifying noise in the digital image provide an output image which is the sum of the input image and its adaptively-amplified high frequency components, which adaptively amplified high frequency components are obtained by subtracting a low-pass filtered image from the input image. Abstract), the method comprising: determining a block (selector 15 obtains a predetermined, select group of pixel values which are disposed in the vicinity of the pixel value to be enhanced, i.e., "filter support" W, from array 5 and applies it as input to low-pass filter 30 and squarer 40. Page 6 line 18) of non-sharpened upsampled pixels for the image region according to the method as recited in claim 14 (Method and apparatus for sharpening edges and details of a digital image without amplifying noise in the digital image provide an output image which is the sum of the input image and its adaptively-amplified high frequency components, which adaptively amplified high frequency components are obtained by subtracting a low-pass filtered image from the input image. Abstract); determining a block (selector 15 obtains a predetermined, select group of pixel values which are disposed in the vicinity of the pixel value to be enhanced, i.e., "filter support" W, from array 5 and applies it as input to low-pass filter 30 and squarer 40. Page 6 line 18) of sharpened upsampled pixels for the image region (the select group of pixel values surrounding the pixel value to be enhanced, i.e., point (i,j). Page 3 line 52); determining one or more indications of contrast for the image region based on the input pixels (Next, an adaptive amplification factor s which varies from 0 to 1 is determined using the generalized statistical variance of pixel values around the pixel value to be enhanced (which reads on “indications of contrast”), v(i,j), and an estimate of the image noise power of the system (block 260, FIG. 2). Page 7 line 52); and determining each of the output pixels of the block of output pixels by performing a respective weighted sum (
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. Page 3 line 43) of: (i) a corresponding non-sharpened upsampled pixel in the block of non-sharpened upsampled pixels (Next, the output enhanced pixel value y(i,j) is determined by adding the input pixel value to be enhanced to the sharpened high frequency component of the pixel value to be erhanced (block 280, FIG.2):
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. Page 7 line 58) and (ii) a corresponding sharpened upsampled pixel in the block of sharpened upsampled pixels (
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. Page 3 line 43), wherein the weights of the weighted sums are based on the determined one or more indications of contrast for the image region (Next, an adaptive amplification factor s which varies from 0 to 1 is determined using the generalized statistical variance of pixel values around the pixel value to be enhanced (which reads on “indications of contrast”), v(i,j), and an estimate of the image noise power of the system (block 260, FIG. 2). Page 7 line 52.
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It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lu to incorporate the teachings of Song to apply adaptive sharpening to input pixels by performing a respective weighted sum of: (i) a corresponding non-sharpened upsampled pixel in the block of non-sharpened upsampled pixels and (ii) a corresponding sharpened upsampled pixel in the block of sharpened upsampled pixels, wherein the weights of the weighted sums are based on the determined indications of contrast for the image region in order to enhance the upsampled images.
Apparatus claim 18 is drawn to the apparatus corresponding to the method of using same as claimed in claim 16. Therefore apparatus claim 18 corresponds to method claim 16, and is rejected for the same reasons of obviousness as used above.
Claims 15 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lu (Color Filter Array Demosaicking: New Method and Performance Measures, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 10, OCTOBER 2003) hereinafter Lu, in view of Tovey (US Patent Pub. No.: US 2023/0298133 A1), hereinafter Tovey.
Regarding claim 15, Lu teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Tovey teaches wherein the upsampled pixels in the block of upsampled pixels are sharpened upsampled pixels (Referring to FIG. 6, a sharpening component 524 of the sharpening stage 613 takes as input the output buffer 506-2 comprising the upscaled frame 508. [0102]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lu to incorporate the teachings of Tovey to sharpen the upsampled pixels in order to improve the quality of the upsampled images.
Regarding claim 19, Tovey in the combination teaches a non-transitory computer readable storage medium having stored thereon computer readable code configured to cause the method of claim 1 to be performed when the code is run on at least one processor (A computer readable storage medium may include any non-transitory storage medium, or combination of non-transitory storage media, accessible by a computer system during use to provide instructions and/or data to the computer system. [0116]).
Claim 20 is claim 1 with additional limitations of “an integrated circuit definition dataset that, when processed in an integrated circuit manufacturing system, configures the integrated circuit manufacturing system to manufacture a processing module”, therefore, the rejection of claim 1 is incorporated herein. Tovey in the combination teaches an integrated circuit definition dataset that, when processed in an integrated circuit manufacturing system, configures the integrated circuit manufacturing system to manufacture a processing module (In some implementations, the apparatus and techniques described above are implemented in a system including one or more integrated circuit (IC) devices (also referred to as integrated circuit packages or microchips). [0115]).
Conclusion
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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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