DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/26/2026 has been entered.
Notice to the Applicant
Limitations appearing inside {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
Claims 1-20 are currently pending.
Response to Amendments
The Amendment filled 05/29/2026 in response to the Final Office Action mailed 02/24/2026 has been entered. Claims 1, 15, and 17 have been amended. The Rejections under 35 USC §103 of claims 1-16 are withdrawn in light of the amended claims.
Response to Arguments/Remarks
Applicant submits that Shibata does not disclose uncertainty metrics for the corruption operator f. Shibata specifies that the regularization term is for the image quality. See Remarks (05/26/2026), page 9. The specification of the instant application discloses that the corruption operator of a camera may be known by observing corruptions of a known image. In other words, the uncertainty of the corruption operator f is initialized using known images; see at least specification (¶[27]). Shibata discloses regularization term (i.e., uncertainty) of the image in at least (¶[0106]). While the regularization function defines the strength of the known image (Shibata, ¶[0067]), thereby characterizing the camera degradation via the known image quality, the regularization term is not applied to the blurring function. Examiner respectfully agrees.
Applicant asserts that DeWeert also fails to disclose “updating …the initial value of the [PSF estimate]”, and . See Remarks (05/26/2026). DeWeert teaches computing “a more accurate estimate of LSF from the EOP, the initial estimate d0 is refined iteratively” (¶¶[0087]-[0088]). See excerpt below, where r is the regularization parameter applied to the DT, the estimate of the PSF. Examiner respectfully disagrees.
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DeWeert further teaches “the deblurring the image based on the estimated PSF is accomplished by noise-regularized Compressive Imaging (CI) deblur processing. The method 1400 may include that the step of generating the estimated PSF from the at least one LSF further comprises fitting, with the at least one processor, a PSF model to the at least one LSF to generate the estimated PSF, which is shown generally at 1420.” (¶[0112]). DeWeert teaches that the “PSF is a sum of separable PSFs” (¶[0100]) and that “the PSF may be estimated from one or a multiplicity of LSFs by various methods”, but does not explicitly disclose a likelihood distribution for the estimated corruption operator f that defines a probability that the estimated corruption operator f can take for each of a plurality of different operator values”. Examiner respectfully agrees that DeWeert does not explicitly disclose the combined limitations of claims 1 and 15. Examiner respectfully disagrees that DeWeert does not disclose the combined limitations of claim 17.
Claim Interpretation
The limitation in claim 1, “a plurality of different operator values” is interpreted by specification of the instant application regarding the HDR blurring model. The different operator values are represented by sensor readings corresponding to exposure time of a series of N shots (¶[20]), shown below:
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Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 17 is rejected under 35 U.S.C. 103 as being unpatentable over “Shibata” (Shibata et. al., US 20170316542 A1) in view of “DeWeert” ( DeWeert et al., US 20190228506 A1).
Regarding claim 17, Shibata teaches a method comprising:
generating, by a camera, a corrupted image of a known input (Shibata ¶[0036]; image receiving unit 10 receives an input image from other device such as a camera or a scanner; ¶[0111] the input image [Y] is, in general, a blurred image);
{accessing one or more initial uncertainty metrics for an estimated corruption operator f associated with the camera and that quantify an uncertainty in the estimate of the estimated corruption operator f};
determining, based on the one or more initial uncertainty metrics and on a difference between an estimated corrupted image of the known input and the generated corrupted image of the known input (Shibata, see Eq 7 and Eq 9, shown above, and ¶[0106-0107]l the optimization function (E(X)), is the sum of the determined regularization term Ereg([X]) and an error term Edata([X]); and see Fig 9, image reconstruction unit 250 updating the training information receiving unit 210. The process is iterative implying that an initial estimate f will be determined in the first iteration), an initial estimate of the estimated corruption operator of having an estimated initial value (Shibata, ¶[0109]; The blurring function is set in advance in the information processing device 500 as a function determined by a user of the information processing device 500.);
{updating, based on the initial estimate of the estimated corruption operator f, at least one of the one or more initial uncertainty metrics for the estimated corruption operator f associated with the camera}; and
storing, in association with the camera, the initial estimate of the corruption operator f {and the one or more uncertainty metrics} (Shibata, See Fig 1 and ¶[0044]; image processing unit 20 may include a storage unit which is not illustrated, each component may store each information)
Shibata does not explicitly disclose
accessing one or more initial uncertainty metrics for an estimated corruption operator f associated with the camera and that quantify an uncertainty in the estimate of the estimated corruption operator f;
updating, based on the initial estimate of the estimated corruption operator f, at least one of the one or more initial uncertainty metrics for the estimated corruption operator f associated with the camera; and
storing … the one or more uncertainty metrics.
However, DeWeert, a similar field of endeavor of image deblurring, teaches
accessing one or more initial uncertainty metrics for an estimated corruption operator f associated with the camera and that quantify an uncertainty in the estimate of the estimated corruption operator f (DeWeert, ¶[0078]; The system 10 may set a regularization parameter ε. The system 10 may compute at least one line spread function (LSF) via noise-regularized inversion of an integration operator operating on the EIP; The system 10 may determine a value of a local curvature regularization parameter, r, resulting in the first noise value and the second noise value being within a tolerance range. The system 10 may generate the estimated PSF from the at least one LSF. ¶[0112]; generating, with the at least one processor, the estimated PSF from the at least one LSF, which is shown generally at 1406. The method 1400 may include building, with the at least one processor, leaky integration differentiation operators, which is shown generally at 1408. The method 1400 may include setting, with the at least one processor, a regularization parameter (i.e., uncertainty metric), which is shown generally at 1410.);
updating, based on the initial estimate of the estimated corruption operator f, at least one of the one or more initial uncertainty metrics for the estimated corruption operator f associated with the camera (DeWeert, ¶¶[0087]-[0088]); “a more accurate estimate of LSF from the EOP, the initial estimate d0 is refined iteratively”); and
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storing … the one or more uncertainty metrics (DeWeert, ¶0121]; data structures may be stored in computer-readable media in any suitable form).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include regularized PSF estimate and iteratively update the PSF as taught by DeWeert to the invention of Shibata. The motivation to do so would be to improve estimation of PSFs for image deblurring in a noise-robust manner.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to store the uncertainty metrics as taught by as taught by DeWeert to the invention of Shibata. The motivation to do so would be to remove bad pixels as they arise.
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shibata in view of DeWeert, and further in view of Gupta et. al., US 10148893 B2, hereinafter Gupta.
Regarding claim 18, the combination of Shibata and DeWeert teaches the method of Claim 17. The combination does not explicitly disclose wherein the corrupted image of the known input comprises a plurality of corrupted images of the known input.
However, Gupta teaches wherein the corrupted image of the known input comprises a plurality of corrupted images of the known input (Gupta [Col 6:9-16]; capture multiple low dynamic range images (e.g., multiple images captured from multiple different exposures of the image sensor) such as would be captured, for example, in a burst mode of some digital cameras).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include multiple images of known input as taught by Gupta to the combined invention of Shibata and DeWeert. The motivation to do so would be to automatically determine settings.
Regarding claim 19. The combination of Shibata and DeWeert teaches the method of Claim 17. The combination does not explicitly disclose further comprising accessing one or more image-capture parameters [Symbol font/0x71], wherein the initial estimate of the corruption operator f is further determined based on the one or more image-capture parameters [Symbol font/0x71] .
However Gupta, a similar field of endeavor of high dynamic range imaging, teaches further comprising accessing one or more image-capture parameters [Symbol font/0x71], wherein the initial estimate of the corruption operator f is further determined based on the one or more image-capture parameters [Symbol font/0x71] (Gupta, [Col 17:40-54]; process 500 can use any suitable properties to select an exposure scheme, such as brightness of the scene (e.g., irradiance of light from the scene), scene/camera motion, and image sensor parameters (e.g., read-noise level, bit-depth, full-well capacity, read-out speed of the camera, whether the camera reads out image data destructively or non-destructively, and/or any other suitable image sensor parameters). For example, if the scene is relatively evenly illuminated (e.g., has a relatively low dynamic range on the order of <10.sup.3), process 500 can select to capture video using a low dynamic range scheme (e.g., single exposures)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include capturing image parameters as taught by Gupta to the combined invention of Shibata and DeWeert. The motivation to do so would be to set an exposure scheme to use based on one or more properties of the image sensor.
Regarding claim 20, the combination of Shibata, DeWeert, and Gupta teach the method of Claim 19. Gupta further teaches further comprising updating the at least one of the one or more initial uncertainty metrics based on the one or more image-capture parameters [Symbol font/0x71] (Gupta, [Col 18:34-46]; the growth rate can be based on a parameter s that can be based on one or more parameters of the image sensor, and can be used to vary the growth rate with respect to both Î.sub.k−1 and {circumflex over (M)}.sub.k−1. For example, for a high quality sensor (e.g., a sensor having low read-noise, large bit depth and/or large full-well capacity), process 500 can implemented with a relatively large value s, while for a low quality sensor (e.g., a sensor having high read-noise, shallow bit depth and/or low full-well capacity) process 500 can implemented with a relatively small value s. In some embodiments, both Î.sub.k−1 and {circumflex over (M)}.sub.k−1 can be normalized such that they each lie in the range of [0,1]).
Allowable Subject Matter
Claim 1-16 are allowed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang (Wang, Ruxin, and Dacheng Tao. "Recent progress in image deblurring." arXiv preprint arXiv:1409.6838 (2014).) provides a review of known techniques for image deblurring.
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/CHANDHANA PEDAPATI/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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