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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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/24/2025 has/have been considered by the examiner.
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(s) 1-7, 9-13 and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singnoo (Ph. D Thesis, University of East Anglia School of Computing Sciences, pp.1-149, 1 October 2012), hereinafter Singnoo in view of Gnanasambandam et al (US 20240257325 A1), hereinafter Gnanasambandam.
-Regarding claim 1, Singnoo discloses a method for generating an image specific global tone curve, the method comprising, at a computing device (Chapters 1-2, 5; FIGS. 2.1-2.2, 2.4, 2.6, 5.1-5.9; Page 101, Sec. 5.2., 1st paragraph, “a global tone-curve is generated by matching, in a least-squares sense, the HDR input and the reference LDR down-sampled using an optimization technique called PAVA”): generating a scatter plot comprising a set of points, each point comprising a gain value and a luma value for a corresponding unit area of the first image (FIGS. 5.3, 5.7; Sec. 5.2.1, pages 102-103, 2nd paragraph; Note: the scatter plots (e.g. Fig. 5.7) show the correlation between each pixel of the LDR and HDR brightness values, hence the two dimensional data {(Xi, Yi)} that are used for the PAVA algorithm correspond to the brightness values in the HDR image (Xi) and the LDR image (Yi) for the same pixel i); determining the image specific global tone curve based on curve fitting to the scatter plot using a cost function (FIGS. 5.2-5.3; Sec.5.2., 1st and 2nd paragraphs, “, a global tone-curve is generated by matching, in a least-squares sense, the HDR input and the reference LDR down-sampled using an optimization technique called PAVA”; Page 106, 1st paragraph; equations (5.8)-(5.9)); and storing the image specific global tone curve with the first image (Sec. 2.5.1, 2nd paragraph: "store control parameter as a 1 D look-up table or as commonly known as Tone-Curve (TC)"; Page 13, 2nd paragraph).
Singnoo does not disclose obtaining a gain map based on a comparison between unit areas of a first image to corresponding unit areas of a second image, the gain map comprising a set of gain values for the unit areas of the first and second images. However, Singnoo does teach generating a global tone-curve by matching a high dynamic range (HDR) input image and the reference low dynamic range (LDR) down-sampled image with PAVA optimization technique. Singnoo’s FIG. 7 shows show the correlation between each pixel of the LDR and HDR brightness values, hence the two dimensional data {(Xi, Yi)} that are used for the PAVA algorithm correspond to the brightness values in the HDR image (Xi) and the LDR image (Yi) for the same pixel i. A person of ordinary skills in the art would understand that a gain map based on a comparison between unit areas of a first image to corresponding unit areas of a second image has to be generated in order to determine the global tone-curve.
In the same field of endeavor, Gnanasambandam teaches a method for tone mapping in high-resolution systems (Gnanasambandam: Abstract; FIGS. 1-17). Gnanasambandam further teaches obtaining a gain map based on a comparison between unit areas of a first image to corresponding unit areas of a second image, the gain map comprising a set of gain values for the unit areas of the first and second images (Gnanasambandam: FIGS. 2-3; [0058]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Singnoo with the teaching of Gnanasambandam by obtaining a gain map in order to determine the global tone-curve.
-Regarding claim 15, Singnoo discloses a non-transitory computer readable storage medium configured to store instructions that, when executed by at least one processor included in a computing device, cause the computing device to generate an image specific global tone curve, by carrying out steps that include (Chapters 1-2, 5; FIGS. 2.1-2.2, 2.4, 2.6, 5.1-5.9; Page 101, Sec. 5.2., 1st paragraph, “a global tone-curve is generated by matching, in a least-squares sense, the HDR input and the reference LDR down-sampled using an optimization technique called PAVA”; one or more processors and memories has to be used in order implement Singnoo’s method shown in FIG. 5.2 and PAVA algorithm): generating a scatter plot comprising a set of points, each point comprising a gain value and a luma value for a corresponding unit area of the first image (FIGS. 5.3, 5.7; Sec. 5.2.1, pages 102-103, 2nd paragraph; Note: the scatter plots (e.g. Fig. 5.7) show the correlation between each pixel of the LDR and HDR brightness values, hence the two dimensional data {(Xi, Yi)} that are used for the PAVA algorithm correspond to the brightness values in the HDR image (Xi) and the LDR image (Yi) for the same pixel i); determining the image specific global tone curve based on curve fitting to the scatter plot using a cost function (FIGS. 5.2-5.3; Sec.5.2., 1st and 2nd paragraphs, “, a global tone-curve is generated by matching, in a least-squares sense, the HDR input and the reference LDR down-sampled using an optimization technique called PAVA”; Page 106, 1st paragraph; equations (5.8)-(5.9)); and storing the image specific global tone curve with the first image (Sec. 2.5.1, 2nd paragraph: "store control parameter as a 1 D look-up table or as commonly known as Tone-Curve (TC)"; Page 13, 2nd paragraph).
Singnoo does not disclose obtaining a gain map based on a comparison between unit areas of a first image to corresponding unit areas of a second image, the gain map comprising a set of gain values for the unit areas of the first and second images. However, Singnoo does teach generating a global tone-curve by matching a high dynamic range (HDR) input image and the reference low dynamic range (LDR) down-sampled image with PAVA optimization technique. Singnoo’s FIG. 7 shows show the correlation between each pixel of the LDR and HDR brightness values, hence the two dimensional data {(Xi, Yi)} that are used for the PAVA algorithm correspond to the brightness values in the HDR image (Xi) and the LDR image (Yi) for the same pixel i. A person of ordinary skills in the art would understand that a gain map based on a comparison between unit areas of a first image to corresponding unit areas of a second image has to be generated in order to determine the global tone-curve.
In the same field of endeavor, Gnanasambandam teaches a method for tone mapping in high-resolution systems (Gnanasambandam: Abstract; FIGS. 1-17). Gnanasambandam further teaches obtaining a gain map based on a comparison between unit areas of a first image to corresponding unit areas of a second image, the gain map comprising a set of gain values for the unit areas of the first and second images (Gnanasambandam: FIGS. 2-3; [0058]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Singnoo with the teaching of Gnanasambandam by obtaining a gain map in order to determine the global tone-curve.
-Regarding claims 2 and 16, Singnoo in view of Gnanasambandam teaches the method of claim 1 and the non-transitory computer readable storage medium of claim 15. The combination further teaches generating a reconstructed gain map from the image specific global tone curve; and determining a residual gain map based on a comparison between unit areas of the reconstructed gain map and corresponding unit areas of the gain map (Singnoo: Sec.5.2.2, 1st-2nd paragraphs; (5.12)).
-Regarding claims 3 and 17, Singnoo in view of Gnanasambandam teaches the method of claim 2 and the non-transitory computer readable storage medium of claim 16. The combination further teaches wherein the second image is replicable using a combination of the first image, the image specific global tone curve, and the residual gain map (Singnoo: FIG. 5.2; Sec.5.2; equations (5.1)-(5.6); (5.12)).
-Regarding claim 4, Singnoo in view of Gnanasambandam teaches the method of claim 1. The combination further teaches wherein the residual gain map comprises a difference between gain values of unit areas of the reconstructed gain map and gain values of corresponding unit areas of the gain map (Singnoo: Sec.5.2.2, 1st-2nd paragraphs; (5.12)).
-Regarding claim 5, Singnoo in view of Gnanasambandam teaches the method of claim 1. The combination further teaches wherein: the first image comprises a standard dynamic range (SDR) image; and the second image comprises a high dynamic range (HDR) image (Singnoo: FIG. 5.2; Sec. 5.2., page 101, 1st paragraph, “HDR input and the reference LDR …”, page 102, last paragraph – page 103, 1st paragraph; A person of ordinary skills in the art would understand that Singnoo in view of Gnanasambandam has no restriction on SDR as the first image ).
-Regarding claim 6, Singnoo in view of Gnanasambandam teaches the method of claim 1. The combination further teaches generating a variant second image based on the first image and the image specific global tone curve (Singnoo: FIG. 5.2; equation (5.12)).
-Regarding claim 7, Singnoo in view of Gnanasambandam teaches the method of claim 6. The combination further teaches wherein the variant second image differs from the second image in one or more channel values of corresponding unit areas (Singnoo: FIG. 5.2, 5.7; equation (5.12)).
-Regarding claim 9, Singnoo in view of Gnanasambandam teaches the method of claim 1. The combination further teaches wherein the curve fitting constrains the image specific global tone curve to be a monotonically increasing function (Singnoo: Sec. 5.2.1., 2nd paragraph: "Given an HDR image and its spatially varying tone-mapped LDR image, we want to find a 1-D surjective and monotonically increasing function that best maps HDR to LDR.").
-Regarding claim 10, Singnoo in view of Gnanasambandam teaches the method of claim 1. The combination further teaches wherein the cost function comprises one of: a least squares function, a structural similarity index measure (SSIM), or a variance inflation factor (VIF) (Singnoo: Sec. 5.2. , 1st -2nd paragraphs; equation (5.8)).
-Regarding claim 11, Singnoo in view of Gnanasambandam teaches the method of claim 1. The combination further teaches wherein channel values of the first image used to generate the scatter plot comprise luminance values of unit areas of the first image (Singnoo: FIGS. 5.2-5.3, 5.7).
-Regarding claim 12, Singnoo in view of Gnanasambandam teaches the method of claim 1. The combination further teaches wherein channel values of the first image used to generate the scatter plot comprise one or more color values of unit areas of the first image (Singnoo: FIGS. 5.2, 5.7; Sec. 5.2., 1st paragraph; Sec. 5.3.1., 1st paragraph: "All processing is carried out in the brightness domain").
-Regarding claim 13, Singnoo in view of Gnanasambandam teaches the method of claim 1. The combination further teaches wherein each unit area corresponds to a single pixel (Singnoo: FIG. 5.7, see caption: "Right column, approximated tone curve (blue) and the scatter plot showing the correlation between each pixel of the LDR and HDR brightness values").
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singnoo (Ph. D Thesis, University of East Anglia School of Computing Sciences, pp.1-149, 1 October 2012), hereinafter Singnoo in view of Gnanasambandam et al (US 20240257325 A1), hereinafter Gnanasambandam, and further in view of Lin et al (US 20230073970 A1), hereinafter Lin.
-Regarding claim 8, Singnoo in view of Gnanasambandam teaches the method of claim 1.
Singnoo in view of Gnanasambandam does not teach wherein the image specific global tone curve comprises multiple contiguous segments, each segment corresponding to a cubic spline.
However, Lin is an analogous art pertinent to the problem to be solved in this application and teaches a method for image generation using nonlinear scaling and tone mapping based on cubic spline curves (Lin: Abstract; FIGS. 1-9). Lin further teaches wherein the image specific global tone curve comprises multiple contiguous segments, each segment corresponding to a cubic spline (Lin: FIGS. 5-7; [0072]-[0073])
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Singnoo in view of Gnanasambandam with the teaching of Lin by using to cubic spline curves for global tone curve in order to more flexible perform the global brightness adjustment and/or the global contrast adjustment (Lin: [0090])
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singnoo (Ph. D Thesis, University of East Anglia School of Computing Sciences, pp.1-149, 1 October 2012), hereinafter Singnoo in view of Gnanasambandam et al (US 20240257325 A1), hereinafter Gnanasambandam, and further in view of Narasimha et al (US 20140152686 A1), hereinafter Narasimha.
-Regarding claim 14, Singnoo in view of Gnanasambandam teaches the method of claim 1.
Singnoo in view of Gnanasambandam does not teach wherein each unit area corresponds to a plurality of contiguous pixels
However, Narasimha is an analogous art pertinent to the problem to be solved in this application and teaches a method of local tone mapping of a high dynamic range (HDR) image (Narasimha: Abstract; FIGS. 1-11). Narasimha further teaches wherein each unit area corresponds to a plurality of contiguous pixels (Narasimha: FIG. 6, 8; [0058]; [0070], “local tone mapping applied to each pixel of an HDR image is based on four neighboring blocks of the pixel”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Singnoo in view of Gnanasambandam with the teaching of Narasimha by performing local tone mapping based on neighboring blocks of the pixel in order to generate HDR image with lesser bit depth (Narasimha: [0024]; [0054]).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singnoo (Ph. D Thesis, University of East Anglia School of Computing Sciences, pp.1-149, 1 October 2012), hereinafter Singnoo in view of Gnanasambandam et al (US 20240257325 A1), hereinafter Gnanasambandam, and further in view of Yuan et al (IEEE International Conference on Computer Vision (ICCV), pp. 2158-2165, 6 November 2011), hereinafter Yuan.
-Regarding claim 18, Singnoo discloses a method for generating an image specific global tone curve, the method comprising, at a computing device (Chapters 1-2, 5; FIGS. 2.1-2.2, 2.4, 2.6, 5.1-5.9; Page 101, Sec. 5.2., 1st paragraph, “a global tone-curve is generated by matching, in a least-squares sense, the HDR input and the reference LDR down-sampled using an optimization technique called PAVA”): generating a scatter plot comprising a set of points, each point comprising a gain value and a luma value for a corresponding unit area of the first image (FIGS. 5.3, 5.7; Sec. 5.2.1, pages 102-103, 2nd paragraph; Note: the scatter plots (e.g. Fig. 5.7) show the correlation between each pixel of the LDR and HDR brightness values, hence the two dimensional data {(Xi, Yi)} that are used for the PAVA algorithm correspond to the brightness values in the HDR image (Xi) and the LDR image (Yi) for the same pixel i); determining the image specific global tone curve based on curve fitting to the scatter plot using a cost function (FIGS. 5.2-5.3; Sec.5.2., 1st and 2nd paragraphs, “, a global tone-curve is generated by matching, in a least-squares sense, the HDR input and the reference LDR down-sampled using an optimization technique called PAVA”; Page 106, 1st paragraph; equations (5.8)-(5.9)); and storing the image specific global tone curve with the first image (Sec. 2.5.1, 2nd paragraph: "store control parameter as a 1 D look-up table or as commonly known as Tone-Curve (TC)"; Page 13, 2nd paragraph).
Singnoo does not disclose obtaining a gain map based on a comparison between unit areas of a first image to corresponding unit areas of a second image, the gain map comprising a set of gain values for the unit areas of the first and second images. However, Singnoo does teach generating a global tone-curve by matching a high dynamic range (HDR) input image and the reference low dynamic range (LDR) down-sampled image with PAVA optimization technique. Singnoo’s FIG. 7 shows show the correlation between each pixel of the LDR and HDR brightness values, hence the two dimensional data {(Xi, Yi)} that are used for the PAVA algorithm correspond to the brightness values in the HDR image (Xi) and the LDR image (Yi) for the same pixel i. A person of ordinary skills in the art would understand that a gain map based on a comparison between unit areas of a first image to corresponding unit areas of a second image has to be generated in order to determine the global tone-curve.
In the same field of endeavor, Gnanasambandam teaches a method for tone mapping in high-resolution systems (Gnanasambandam: Abstract; FIGS. 1-17). Gnanasambandam further teaches obtaining a gain map based on a comparison between unit areas of a first image to corresponding unit areas of a second image, the gain map comprising a set of gain values for the unit areas of the first and second images (Gnanasambandam: FIGS. 2-3; [0058]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Singnoo with the teaching of Gnanasambandam by obtaining a gain map in order to determine the global tone-curve.
Singnoo in view of Gnanasambandam does not teach identifying a plurality of areas of the first image and generating scatter plot and image specific global tone curve for each area of the first image.
Yuan is an analogous art pertinent to the problem to be solved in this application and teaches a method for high quality image reconstruction from raw and JPEG image pair (Yuan: Abstract; FIGS. 1-8). Yuan further teaches identifying a plurality of areas of the first image and generating scatter plot and image specific global tone curve for each area of the first image (Yuan: FIGS. 3(b); Sec. 4.1., 3rd-4th paragraphs, “… underlying algorithm regards the locally nonlinear curve as the combination of multiple piecewise-linear components …”; Page 2163, 1st Col., 2nd paragraph; Note: the Ri and Ji values of the pixels within the patch and with a tone close to the tone of the patch center are used to determine a local linear mapping curve; the tone curve parameters are computed for all local patches in the image).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Singnoo in view of Gnanasambandam with the teaching of Yuan by identifying a plurality of areas of the first image and generating scatter plot and image specific global tone curve for each area of the first image in order to enable faster quick shooting with both richer information and higher resolution in a reconstructed image (Yuan: Abstract).
-Regarding claim 19, Singnoo in view of Gnanasambandam, and further in view of Yuan teaches the method of claim 18.
Singnoo in view of Gnanasambandam does not teach comprising, prior to generating the scatter plots and image specific global tone curves: identifying respective weights to be applied to one or more pixels of the first image, the second image, and/or the gain map; and applying the respective weights to the one or more pixels.
Yuan is an analogous art pertinent to the problem to be solved in this application and teaches a method for high quality image reconstruction from raw and JPEG image pair (Yuan: Abstract; FIGS. 1-8). Yuan further teaches comprising, prior to generating the scatter plots and image specific global tone curves: identifying respective weights to be applied to one or more pixels of the first image, the second image, and/or the gain map; and applying the respective weights to the one or more pixels (Yuan: Sec. 4.1., 4th – 5th paragraphs; Note: the pixels in each patch are weighted according to their tone value distance from the tone value of the center before estimating the tone curve parameters).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Singnoo in view of Gnanasambandam with the teaching of Yuan by identifying a plurality of areas of the first image and generating scatter plot and image specific global tone curve for each area of the first image in order to enable faster quick shooting with both richer information and higher resolution in a reconstructed image (Yuan: Abstract).
-Regarding claim 20, Singnoo in view of Gnanasambandam, and further in view of Yuan teaches the method of claim 18.
Singnoo in view of Gnanasambandam does not teach receiving a selection of an image specific global tone curve among the image specific global tone curves; and applying the image specific global tone curve against the first image.
Yuan is an analogous art pertinent to the problem to be solved in this application and teaches a method for high quality image reconstruction from raw and JPEG image pair (Yuan: Abstract; FIGS. 1-8). Yuan further teaches receiving a selection of an image specific global tone curve among the image specific global tone curves; and applying the image specific global tone curve against the first image (Yuan: Sec. 4.1., Page 2160, 1st Col., 3rd paragraph; Note: for each local patch the corresponding curve parameters
a
k
,
b
k
,
are selected and applied on the JPEG image to get the mapped RAW image).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Singnoo in view of Gnanasambandam with the teaching of Yuan by identifying a plurality of areas of the first image and generating scatter plot and image specific global tone curve for each area of the first image in order to enable faster quick shooting with both richer information and higher resolution in a reconstructed image (Yuan: Abstract).
Conclusion
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/XIAO LIU/Primary Examiner, Art Unit 2664
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