DETAILED ACTION
Response to Arguments
The amendment filed 15 October 2025 has been entered in full. Accordingly, claims 1, 3-11, and 13-20 are pending in the application.
Regarding the rejections under 35 U.S.C. 103, the applicant amends independent claims 1 and 11 to recite “wherein the image downscaling processing is one of: super sampling, nearest neighbor, bell, hermite, mitchell, or bilinear downscaling.” The applicant argues that the prior art of record does not disclose or suggest this limitation. In response, after an updated search, the examiner relies on newly found reference Zhang et al. (Fast Image Dehazing Using Guided Filter, 2015, Proceedings of ICCT, Pages 182-185), hereinafter “Zhang”, in a new grounds of rejection as necessitated by the applicant’s amendment.
Claim Objections
Claim 11 is objected to because of the following informalities: new limitation “wherein the image downscaling processing is one of: nearest neighbor, bell, hermite, mitchell, or bilinear downscaling” is recited along with similar original limitation “and wherein the image downscaling processing is one of: super sampling, bicubic, nearest neighbor, bell, hermite, mitchell, or bilinear downscaling”. Appropriate correction is required.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 7, 11-14, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Riaz (Single image dehazing via reliability guided fusion, 2016, J. Vis. Commun. Image R., Vol. 40, Pages 85-97) in view of Zhang (Fast Image Dehazing Using Guided Filter, 2015, Proceedings of ICCT, Pages 182-185).
Claim 1 is met by the combination of Riaz and Zhang, wherein
Riaz teaches:
A method for haze reduction in images (See the Abstract.), comprising:
accessing an image of an object obscured by haze, the image having an original resolution (See the hazy image in the proposed method in Fig. 1 on page 87.);
downscaling the image to provide a downscaled image having a lower resolution than the original resolution (See the downscaling in Fig. 1 on page 87 and page 86, left column: “For real-time handling of high-resolution images, we propose an efficient downscaling technique.”);
processing the downscaled image to generate a dehazing parameter corresponding to the lower resolution (See the transmission map in Fig. 1 on page 87 and page 86, left column: “The transmission map estimated at lower resolution…”.);
converting the dehazing parameter corresponding to the lower resolution to a second dehazing parameter corresponding to the original resolution (See the upscaling in Fig. 1 on page 87 and page 86, left column: “The transmission map estimated at lower resolution is upscaled…”.); and
dehazing the image based on the second dehazing parameter corresponding to the original resolution (See the haze-free image in Fig. 1 on page 87 and page 86, left column: “The transmission map estimated at lower resolution is upscaled and used with the original image for recovering a haze-free image.”),
wherein the downscaling is based on image downscaling processing, and wherein the converting is based on an inverse of the image downscaling processing (See Fig. 1, upscaling is the inverse of downscaling.)…
Riaz does not disclose the following; however, Zhang teaches:
wherein the image downscaling processing is one of: super sampling, nearest neighbor, bell, hermite, mitchell, or bilinear downscaling (See Fig. 2 and page 183: “At fIrst, calculating the atmospheric light and rough transmission, next the rough transmission map and the guidance image employ nearest neighbor interpolation down-sampling. After guided filter processing, the guided fIlter output image employs bilinear interpolation up-sampling.”).
Riaz and Zhang together teach the limitations of claim 1. Zhang is directed to a similar field of art (reducing the processing load of guided filter image dehazing). Therefore, Riaz and Zhang are combinable. Modifying the system and method of Riaz by simple substitution of the downsampling in Riaz for the nearest neighbor interpolation downsampling of Zhang would yield the expected and predictable result of reduced computational complexity and memory use. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Riaz and Zhang in this way.
Claim 3 is met by the combination of Riaz and Zhang, wherein
The combination of Riaz and Zhang teaches:
The method of claim 1, wherein processing the downscaled image includes:
And Riaz further teaches:
estimating an atmospheric light component value for the downscaled image (See page 86, right column: “For a given input image, we limit the contrast boost of well-connected surfaces that closely resembles atmospheric light A, like sky regions. This is referred to as ‘sky handling’ (block in gray) part of our algorithm.”); determining a dark channel matrix of the downscaled image (See the pixel dark channel and fine dark channel in Fig. 1 on page 87.); and determining a transmission map for the downscaled image according to the atmospheric light component and the dark channel matrix (See in Fig. 1, application of sky handling (atmospheric light) and fine dark channel to determine the fine transmission map.).
Claim 4 is met by the combination of Riaz and Zhang, wherein
The combination of Riaz and Zhang teaches:
The method of claim 3, wherein converting the dehazing parameter corresponding to the lower resolution to the second dehazing parameter corresponding to the original resolution includes
And Riaz further teaches:
converting the transmission map for the downscaled image to a second transmission map for the original image (See the upscaling in Fig. 1 on page 87 and page 86, left column: “The transmission map estimated at lower resolution is upscaled…”.).
Claim 7 is met by the combination of Riaz and Zhang, wherein
The combination of Riaz and Zhang teaches:
The method of claim 3, wherein determining the transmission map for the downscaled image includes
And Riaz further teaches:
determining, for each pixel x of the downscaled image:
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where: w is a predetermined constant, I_DARK(x) is a value of the dark channel matrix for the pixel x, and A is the atmospheric light component value (See Eq. 22 on page 90:
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. The atmospheric light component value A is accounted for by Eq. 23 and 24, as explained on pages 90-91 up to section 3.4.).
Claim 11 is met by the combination of Riaz and Zhang for the reasons given in the treatment of claim 1. Riaz further teaches:
A system for haze reduction in images, comprising: an imaging device configured to capture an image of an object obscured by haze; a display device; a processor; and a memory storing instructions which, when executed by the processor (See the Abstract and page 94: “The experiments were carried out in Matlab running on a 3.4 GHz Intel i7 processor.”), cause the system to:
access the image of the object obscured by haze, the image having an original resolution (See the hazy image in the proposed method in Fig. 1 on page 87.),
downscale the image to provide a downscaled image having a lower resolution than the original resolution (See the downscaling in Fig. 1 on page 87 and page 86, left column: “For real-time handling of high-resolution images, we propose an efficient downscaling technique.”),
process the downscaled image to generate a dehazing parameter corresponding to the lower resolution (See the transmission map in Fig. 1 on page 87 and page 86, left column: “The transmission map estimated at lower resolution…”.),
convert the dehazing parameter corresponding to the lower resolution to a second dehazing parameter corresponding to the original resolution (See the upscaling in Fig. 1 on page 87 and page 86, left column: “The transmission map estimated at lower resolution is upscaled…”.),
dehaze the image based on the second dehazing parameter corresponding to the original resolution (See the haze-free image in Fig. 1 on page 87 and page 86, left column: “The transmission map estimated at lower resolution is upscaled and used with the original image for recovering a haze-free image.”), and
display the de-hazed image on the display device (See page 90, left column, implied display of the dehazed image: “As subjective evaluation of the image quality heavily depends on a display monitor’s settings, ambient light, and viewing angles, a quantitative comparison of the ground-truth and recovered scene radiance by several methods have been presented in Section 4.”),
wherein the downscaling is based on image downscaling processing (See Fig. 1.), and wherein the image downscaling processing is one of: super sampling, bicubic, nearest neighbor, bell, hermite, mitchell, or bilinear downscaling (***The examiner treats the inclusion of this limitation as an error and disregards it.),
wherein the converting is based on an inverse of the image downscaling processing (See Fig. 1, upscaling is the inverse of downscaling.), and
Riaz does not disclose the following; however, Zhang teaches:
wherein the image downscaling processing is one of: nearest neighbor, bell, hermite, mitchell, or bilinear downscaling (See Fig. 2 and page 183: “At fIrst, calculating the atmospheric light and rough transmission, next the rough transmission map and the guidance image employ nearest neighbor interpolation down-sampling. After guided filter processing, the guided fIlter output image employs bilinear interpolation up-sampling.”).
Riaz and Zhang together teach the limitations of claim 1. Zhang is directed to a similar field of art (reducing the processing load of guided filter image dehazing). Therefore, Riaz and Zhang are combinable. Modifying the system and method of Riaz by simple substitution of the downsampling in Riaz for the nearest neighbor interpolation downsampling of Zhang would yield the expected and predictable result of reduced computational complexity and memory use. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Riaz and Zhang in this way.
Claim 12 is met by the combination of Riaz and Zhang for the reasons given in the treatment of claim 2.
Claim 13 is met by the combination of Riaz and Zhang for the reasons given in the treatment of claim 3.
Claim 14 is met by the combination of Riaz and Zhang for the reasons given in the treatment of claim 4.
Claim 17 is met by the combination of Riaz and Zhang for the reasons given in the treatment of claim 7.
Claim(s) 5, 6, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Riaz (Single image dehazing via reliability guided fusion, 2016, J. Vis. Commun. Image R., Vol. 40, Pages 85-97) in view of Zhang (Fast Image Dehazing Using Guided Filter, 2015, Proceedings of ICCT, Pages 182-185) in view of Liu 1 (Image Dehazing Based on Region Growing, 2017, 4th International Conference on Systems and Informatics, Pages 192-197).
Claim 5 is met by the combination of Riaz, Zhang, and Liu 1, wherein
The combination of Riaz and Zhang teaches:
The method of claim 4, wherein
The combination of Riaz and Zhang does not disclose the following; however, Liu 1 teaches:
dehazing the image includes: converting the image from at least one of an RGB image, a CMYK image, a CIELAB image, or a CIEXYZ image to a YUV image; performing a de-hazing operation on the YUV image to provide a Y'UV image; and converting the Y'UV image to the de-hazed image (See page 195: “In addition, we employ the method of down sample, and replace RGB with the luminance Y component of YUV color space to evaluate transmission.” Then see the conversion of the dehazed YUV image to RGB in Fig. 10.).
Riaz, Zhang, and Liu 1 together teach the limitations of claim 5. Liu 1 is directed to a similar field of art (image dehazing with downscaling and YUV conversion steps). Therefore, Riaz, Zhang, and Liu 1 are combinable. Modifying the system and method of Riaz by adding the capability of “dehazing the image includes: converting the image from at least one of an RGB image, a CMYK image, a CIELAB image, or a CIEXYZ image to a YUV image; performing a de-hazing operation on the YUV image to provide a Y'UV image; and converting the Y'UV image to the de-hazed image”, as taught by Liu 1, would yield the expected and predictable result of improved dehazing results. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Riaz, Zhang, and Liu 1 in this way.
Claim 6 is met by the combination of Riaz, Zhang, and Liu 1, wherein
The combination of Riaz, Zhang, and Liu 1 teaches:
The method of claim 5, wherein
And Liu 1 further teaches:
performing the de-hazing operation on the YUV image includes, for each pixel x in the YUV image:
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where: T_N(x) is a value of the second transmission map corresponding to the pixel x, and A is the atmospheric light component value for the downscaled image (See Eq. 3 on page 193.).
See the motivation to combine in the treatment of claim 5.
Claim 15 is met by the combination of Riaz, Zhang, and Liu 1 for the reasons given in the treatment of claim 5.
Claim 16 is met by the combination of Riaz, Zhang, and Liu 1 for the reasons given in the treatment of claim 6.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Riaz (Single image dehazing via reliability guided fusion, 2016, J. Vis. Commun. Image R., Vol. 40, Pages 85-97) in view of Zhang (Fast Image Dehazing Using Guided Filter, 2015, Proceedings of ICCT, Pages 182-185) in view of Kim (Optimized contrast enhancement for real-time image and video dehazing, 2013, J. Vis. Commun. Image R., Vol. 24, Pages 410-425), as cited in the IDS filed 12 July 2022.
Claim 8 is met by the combination of Riaz, Zhang, and Kim, wherein
The combination of Riaz and Zhang teaches:
The method of claim 3, wherein
The combination of Riaz and Zhang does not disclose the following; however, Kim teaches:
estimating the atmospheric light component value for the downscaled image includes, for a block of pixels in the downscaled image: determining if a width times height for the block of pixels is greater than a predetermined threshold value, in a case where the width times height is greater than the predetermined threshold value: dividing the block of pixels into a plurality of smaller pixel areas, calculating a mean value and a standard deviation for pixel values of each of the smaller pixel areas, determining a score for each of the smaller pixel areas based on the mean value minus the standard deviation for the smaller pixel area, and identifying one of the plurality of smaller pixel areas having a highest score among the scores; and in a case that the width times height is not greater than the predetermined threshold value, estimating the atmospheric light component value as a darkest pixel in the block of pixels (See page 411: “In addition, we propose a hierarchical searching method based on the quad-tree subdivision. More specifically, as illustrated in Fig. 2, we first divide an input image into four rectangular regions. We then define the score of each region as the average pixel value subtracted by the standard deviation of the pixel values within the region. Then, we select the region with the highest score and divide it further into four smaller regions. We repeat this process until the size of the selected region is smaller than a pre-specified threshold. For example, in Fig. 2, the red block is finally selected. Within the selected region, we choose the color vector, which minimizes the distance kðIrðpÞ; Ig ðpÞ; IbðpÞÞ ð255; 255; 255Þk, as the atmospheric light. By minimizing the distance from the pure white vector ð255; 255; 255Þ, we attempt to choose the atmospheric light that is as bright as possible.”).
Riaz, Zhang, and Kim together teach the limitations of claim 8. Kim is directed to a similar field of art (image and video dehazing). Therefore, Riaz, Zhang, and Kim are combinable. Modifying the system and method of Riaz & Zhang by adding the capability of estimating atmospheric light in the way claimed above, as taught by Kim, would yield the expected and predictable result of improved atmospheric light component value estimation for a clearer and natural haze-free image. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Riaz, Zhang, and Kim in this way.
Claim 18 is met by the combination of Riaz, Zhang, and Kim for the reasons given in the treatment of claim 8.
Claim(s) 9, 10, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Riaz (Single image dehazing via reliability guided fusion, 2016, J. Vis. Commun. Image R., Vol. 40, Pages 85-97) in view of Zhang (Fast Image Dehazing Using Guided Filter, 2015, Proceedings of ICCT, Pages 182-185) in view of Liu 2 (Sky detection- and texture smoothing-based high-visibility haze removal from images and videos, 19 March 2017, Pages 1-10).
Claim 9 is met by the combination of Riaz, Zhang, and Liu 2, wherein
The combination of Riaz and Zhang teaches:
The method of claim 3, wherein
The combination of Riaz and Zhang does not disclose the following; however, Liu 2 teaches:
estimating the atmospheric light component value includes smoothing the atmospheric light component value based on an estimated atmospheric light component value for a previous dehazed image frame (See page 5: “Nevertheless, in order to prevent the atmospheric lights from the influence of sudden S̄L(n) changes, we further slow down the changes of the atmospheric lights as
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where A(n) is the atmospheric light of frame n ⩾ 2 and the damping coefficient 𝛼 is taken as 0.95 here.”).
Riaz, Zhang, and Liu 2 together teach the limitations of claim 9. Liu 2 is directed to a similar field of art (image and video dehazing.). Therefore, Riaz, Zhang, and Liu 2 are combinable. Riaz only appears to consider single image dehazing. One problem in video dehazing is flickering phenomena when atmospheric light values change from frame to frame. Modifying the system and method of Riaz by adding the capability to process videos and “[estimate] the atmospheric light component value [which] includes smoothing the atmospheric light component value based on an estimated atmospheric light component value for a previous dehazed image frame”, as taught by Liu 2, would yield the expected and predictable result of slowing down atmospheric light changes for an improved dehazing result. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Riaz, Zhang, and Liu 2 in this way.
Claim 10 is met by the combination of Riaz, Zhang, and Liu 2, wherein
The combination of Riaz, Zhang, and Liu 2 teaches:
The method of claim 9, wherein
And Liu 2 further teaches:
smoothing the atmospheric light component value includes determining the atmospheric light component value as:
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where: A-CUR is the estimated atmospheric light component value for the downscaled image, A-PRE is the estimated atmospheric light component value for a previous downscaled image, and coef is a predetermined smoothing coefficient (See page 5: “Nevertheless, in order to prevent the atmospheric lights from the influence of sudden S̄L(n) changes, we further slow down the changes of the atmospheric lights as
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where A(n) is the atmospheric light of frame n ⩾ 2 and the damping coefficient 𝛼 is taken as 0.95 here.”).
See the motivation to combine in the treatment of claim 9.
Claim 19 is met by the combination of Riaz, Zhang, and Liu 2 for the reasons given in the treatment of claim 9.
Claim 20 is met by the combination of Riaz, Zhang, and Liu 2 for the reasons given in the treatment of claim 10.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN S LEE whose telephone number is (571)272-1981. The examiner can normally be reached 11:30 AM - 7:30 PM.
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/Jonathan S Lee/Primary Examiner, Art Unit 2677