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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) filed on January 31, 2025, has been considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
In paragraph [0142], “illustrate din” should read “illustrated in”.
Appropriate correction is required.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. (US Pub No 20210241421), hereinafter Pan in view of Nobukuni et al. (JP Pub No 2017129952), hereinafter Nobukuni.
As to Claim 1, Pan teaches an apparatus for denoising an image obtained through a multispectral imaging sensor (see Abstract, “Described herein are systems and embodiments for multispectral image demosaicking”, where ‘demosaicking’ is a known term in the art for reducing noise in an image), the apparatus comprising:
a memory configured to store one or more instructions (see Fig. 8, storage devices 808), and
at least one processor (see Fig. 8, CPU 801) configured to execute the one or more instructions to:
divide a wavelength band of an input image into a plurality of sub- wavelength bands, each of the plurality of sub-wavelength bands corresponding to one of a plurality of channels (see paragraph [0008], “In a fourth aspect, the present disclosure provides a system for demosaicking a multispectral image from a multispectral filter arrays (MSFA) sensor with multiple sub-bands”, and see paragraph [0042], “In one or more embodiments, for the proposed MSI demosaicking, a new process is put forward for the estimation of a(x, y, λ) and b(x, y, where λ), refers to the wavelength of a specific filter in MSFA”, where MSFA stands for multispectral filter array);
sub-sample the input image into a plurality of sub- sampled images, each corresponding to one of the plurality of channels (see paragraph [0046], “As shown in the FIG. 5, an input multispectral mosaic image 502 may be separated (605) to a set of sparse subsampled or sub-band images, each corresponding to one filter or sub-band in the MSFA”),
obtain, for each of the plurality of channels, a first denoising image and a differential image the first denoising image obtained by performing a denoising operation on a sub- sampled image for the respective channel (see paragraph [0046], “ A full resolution deep panchromatic image (DPI) is recovered (610) from the multispectral mosaic image using the DPI-Net 512. A subsampled DPI image 514 is obtained (615) from the recovered DPI”, see Fig. 5, the’ first denoising imaging’ 514),
and the differential image obtained based on a difference between the first denoising image and the sub-sampled image for the respective channel (see paragraph [0046], “For one sub-band image 504 (using the subsampled R band image as an example, the sub-band for the sub-band image 504 corresponds to the subsampled DPI image 514), it is first subtracted (620) from the subsampled DPI 514 to get a sparse residual image 516”, and see Fig. 5, the ‘differential image’ 516);
obtain, for each of the plurality of channels, a second denoising image by performing preprocessing on the differential image of the respective channel (see paragraph [0046], “Using the DPI as a guide image 515, this sparse residual image 516 is interpolated (625) to full resolution to obtain an initial demosaicked residual image 517”, where the Examiner has interpreted the ‘interpolation’ as preprocessing)
and generate an output image by summing the first denoising image and the second denoising image obtained for each of the plurality of channels (see paragraph [0046], “Using the DPI as a guide image 515, this sparse residual image 516 is interpolated (625) to full resolution to obtain an initial demosaicked residual image 517, which is then added back (630) to the DPI to get the first-pass demosaicked image 523 corresponding to the R band”).
Pan fails to teach performing principal component analysis and projection on the preprocessed differential image. However, in an analogous art, Nobukuni teaches a method for reducing noise in image data (see Abstract, “To provide a data processor capable of simplifying a series of noise removal processing based on the relationship in every dimensions in a piece of multidimensional data such as image data”)
which comprises obtaining image data corresponding to a specific wavelength band (see paragraph [0038], “The band separation unit 210 separates the image data for each band and outputs the separated image data”)
and then performing principal component analysis and projection on the image data (see paragraph [0040], “The noise removing units # 1 (230) to # 3 (250) perform principal component analysis on the band-based image data to remove noise”, and see paragraph [0053], “The projecting unit 235 projects the image data s of interest onto the 1-th principal component axis generated by the 1-th principal component axis generating unit 234 to convert the image data s of interest into image data’s on the 1-th principal component axis”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the principal component analysis taught by Nobukuni with the image processing method taught by Pan. The motivation for doing so would be to reduce noise without causing signal degradation. Nobukuni teaches in paragraph [0065], “In this way, the noise removal processing by the principal component analysis does not cause degradation of the signal due to noise removal, and thus reproducibility of the image data can be improved as compared to the noise removal processing by regression analysis.” Thus, it would have been obvious to combine the PCA taught by Nobukuni with the image processing system taught by Pan in order to obtain the invention as claimed in Claim 1.
As to Claim 3, Pan in view of Nobukuni teaches wherein each of the plurality of sub-sampled images for the respective channels has a size of at least one multispectral filter array (see Pan, paragraph [0042], “First, assuming the MSFA pattern is of m×n, a sliding window size (m+1)×(n+1) is used (405)”, where MSFA stands for multispectral filter array, and see paragraph [0046], “As shown in the FIG. 5, an input multispectral mosaic image 502 may be separated (605) to a set of sparse subsampled or sub-band images, each corresponding to one filter or sub-band in the MSFA”).
As to Claim 11, Pan in view of Nobukuni teaches an operation method of an apparatus for denoising an image obtained through a multispectral imaging sensor (see Pan, paragraph [0001], “the present disclosure relates to systems and methods for multispectral image demosaicking”), the operation method comprising the same steps recited in Claim 1. Therefore, the rejection and rationale are analogous to that of Claim 1.
As to Claim 13, Claim 13 claims the same limitation claimed as Claim 3 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 3.
Claims 2, 4, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. (US Pub No 20210241421), hereinafter Pan, in view of Nobukuni et al. (JP Pub No 2017129952), hereinafter Nobukuni, and further in view of Romanenko et al. (US Pub No 20200396398), hereinafter Romanenko.
As to Claim 2, Pan in view of Nobukuni fails to explicitly teach that the input image includes a visible light band and a non-visible light band, and the plurality of channels comprises at least four channels.
However, in an analogous art, Romanenko teaches a method for denoising a multispectral image (see Abstract, “Devices, methods, and non-transitory program storage devices for spatiotemporal image noise reduction are disclosed, comprising: maintaining an accumulated image in memory; and obtaining a first plurality of multispectral images (e.g., RGB-IR images)”),
wherein the input image comprises visible light band and a non-visible light band (see paragraph [0007], “Imaging sensors capable of capturing multispectral image data including image data in both visible and non-visible wavelength ranges”),
and the plurality of channels comprises at least four channels (see paragraph [0027], “The input image data to a given spatiotemporal NR operation may comprise a first plurality of N-channel multispectral images. In the examples described here, the multispectral images will comprise RGB-IR images”, where the red, green, blue and infrared channel are four total channels).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multispectral sensor taught by Romanenko with the image processing method taught by Pan in view of Nobukuni. The motivation for doing so would be to use the image data from the non-visibile spectrum in order to inform noise reduction. Romanenko teaches in paragraph [0008], “In some embodiments disclosed herein, the combined visible and non-visible light signals of a multispectral imaging sensor may be used as the basis for noise reduction decisions, providing more noise reduction potential than either visible light (or any other singular spectral band) alone. This combined image information, i.e., comprising both visible and non-visible light information, may be used to help guide noise reductions decisions.” Thus, it would have been obvious to combine the multispectral image sensor taught by Romanenko with the teachings of Pan and Nobukuni in order to obtain the invention as claimed in Claim 2.
As to Claim 4, Pan in view of Nobukuni fails to teach wherein the at least one processor is further configured to obtain the first denoising image and the differential image by applying a non-local means (NLM) algorithm to the plurality of sub-sampled images for the respective channels.
However, Romanenko teaches a non-local means algorithm can be applied to multispectral images to reduce noise (see paragraph [0009], “According to some embodiments, a variant of typical noise reduction processes, termed “non-local means” (NLM), may be used in the spatiotemporal NR processing. NLM involves calculating differences between pixel blocks across an extent of an image, and, from those blocks, finding blending weights between pixels to produce output pixels with reduced noise.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the Nosie reduction algorithm taught by Romanenko with the noise reduction method taught by Pan. The motivation for doing so would be to reduce noise throughout the entire image. Romanenko teaches in paragraph [0009], “NLM involves calculating differences between pixel blocks across an extent of an image, and, from those blocks, finding blending weights between pixels to produce output pixels with reduced noise.” Thus, it would have been obvious to combine the NLM algorithm taught by Romanenko with eh teachings of Pan and Nobukuni in order to obtain the invention as claimed in Claim 4.
As to Claim 12, Claim 12 claims the same limitation claimed as Claim 2 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 2.
As to Claim 14, Claim 14 claims the same limitation claimed as Claim 4 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 4.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. (US Pub No 20210241421), hereinafter Pan, in view of Nobukuni et al. (JP Pub No 2017129952), hereinafter Nobukuni, and further in view of Zeng et al. (CN Pub No 112541873), hereinafter Zeng.
As to Claim 5, Pan in view of Nobukuni fails to explicitly teach the at least one processor is further configured to apply a bilateral filter (BF) algorithm and a bilinear interpolation to the differential image of each of the plurality of channels. Pan teaches that the differential image for each channel is processed and interpolated (see paragraph [0046]), but fails to explicitly teach applying bilinear interpolation.
However, in an analogous art, Zeng teaches an image processing method which comprises applying a bilateral filter and bilinear interpolation to an image (see paragraph [0004], “Aiming at the problems in the prior art, the invention provides an image processing method based on a bilateral filter”, and see paragraph [0022], “the original image A is subjected to bilinear interpolation downsampling”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the bilateral filter and bilinear interpolation taught by Zeng with the teachings of Pan and Nobukuni. The motivation for doing so would be to reduce noise while increasing efficiency. Zeng theaches in paragraph [0004], “Aiming at the problems in the prior art, the invention provides an image processing method based on a bilateral filter, which can well reserve the remarkable edges in the image while fast filtering, can well remove noise phenomenon, has better edge protection performance and has higher calculation efficiency”. Thus, it would have been obvious to combine the filtering and interpolation taught by Zeng with the teachings of Pan and Nobukuni inorder to obtain the invention as claimed in Claim 5.
As to Claim 6, Pan teaches that the differential image may be preprocessed (see Pan, paragraph [0046]), but fails to teach wherein the at least one processor is further configured to linearly transform the differential image preprocessed for each of the plurality of channels into a plurality of eigen vectors and a plurality of eigen values by performing the principal component analysis on a plurality of pixels of the differential image preprocessed for each of the plurality of channels.
However, Nobukuni teaches that image data corresponding to a wavelength band may be linearly transformed into a plurality of eigen vectors and a plurality of eigen values by performing the principal component analysis on a plurality of pixels (see paragraph [0057], “In the embodiment of the present technology, principal component analysis is performed for each band of image data. As described above, since the image data is reduced in the separation unit # 1 (211) and the like, in a case where a 7 pixel x 7 pixel image region is selected in the region selecting unit 231, principal component analysis is performed on a range corresponding to 14 pixel x 14 pixel of the mid-region image data”), and see paragraph [0052], “The 1 principal component axis generation unit 234 generates an eigenvector y, which is a unit vector in the same direction as the principal component axis, based on the variance-covariance matrix A generated by the variance-covariance matrix generation unit 233, and outputs the eigenvector y as the 1 principal component axis.”, where it is well-known to those of ordinary skill that a variance -covariance matrix contains eigenvectors and eigenvaleus).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the principal component analysis taught by Nobukuni with the image processing method taught by Pan and Zeng. The motivation for doing so would be to reduce noise without causing signal loss (see Nobukuni, paragraph [0065]). Thus, it would have been obvious to combine the PCA taught by Nobukuni with the image processing system taught by Pan and Zeng in order to obtain the invention as claimed in Claim 6.
As to Claim 15, Claim 15 claims the same limitation claimed as Claim 5 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 5.
As to Claim 16, Claim 16 claims the same limitation claimed as Claim 6 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 6.
Claim(s) 7-9 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. (US Pub No 20210241421), hereinafter Pan, in view of Nobukuni et al. (JP Pub No 2017129952), hereinafter Nobukuni, further in view of Zeng et al. (CN Pub No 112541873), hereinafter Zeng, and further in view of Malini et al. (S. Malini et al., "Image denoising using multiresolution principal component analysis," 2015 Global Conference on Communication Technologies (GCCT), Thuckalay, India, 2015, pp. 4-7), hereinafter Malini.
As to Claim 7, Pan in view of Nobukuni and Zeng fails to explicitly teach wherein the at least one processor is further configured to: determine a priority of the plurality of eigen vectors based on the plurality of eigen values and obtain the second denoising image by selecting and projecting at least one eigen vector from among the plurality of eigen vectors, based on the determined priority. Nobukuni teaches that the a ‘principal component axis’ for projection is chosen through known methods (see paragraph [0041]), but doesn’t explicitly teach how this axis is chosen.
However, in an analogous art, Malini teaches a method of image denoising using principal component analysis (see Abstract, pg. 4, “Using Principal Component Analysis, noisy image is decorrelated so as to get distinction between signal and noise”),
which comprises determining a priority of the plurality of eigen vectors based on the plurality of eigen values and (see pg. 5, Section II, “The Eigen values form a diagonal matrix and Eigen vectors form an orthogonal p×p matrix. The elements of the diagonal matrix are sorted and are re-arranged in descending order…Significance of the second, third, etc. principal components decrease as they correspond to less and less Eigen values in the diagonal matrix, .”)
obtain the second denoising image by selecting and projecting at least one eigen vector from among the plurality of eigen vectors, based on the determined priority (see pg. 5, Section II, “The first principal component corresponds to the first column of PC which has highest significance as it corresponds to the highest Eigen value of the covariance matrix.”, and see pg. 6, Section IV, “The PCA projection of the reshaped matrix is done by the matrix multiplication where A is the reshaped matrix and PC is the principal components”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the vector selection and projection taught by Malini with the image denoising method taught by Pan in view of Nobukuni and Zeng. The motivation for doing so would be to reduce dimensions by deleting insignificant data. Malini teaches on pg. 5, Section III, “By deleting insignificant principal components or by deleting those columns corresponding to low Eigen values of D, dimensionality reduction can be obtained and thereby insignificant data such as noise can be forced to zero or low values.” Thus, it would have been obvious to combine the eigen vector selection taught by Malini with the teachings of Pan and Nobukuni and Zeng in order to obtain the invention as claimed in Claim 7.
As to Claim 8, Pan in view of Nobukuni and Zeng fails to explicitly teach wherein the at least one processor is further configured to obtain the second denoising image by selecting and projecting a first number of eigen vectors of the plurality of eigen vectors.
However, Malini teaches that a number of eigen vectors can be selected (see pg. 5, Section III, “The first principal component corresponds to the first column of which has highest significance as it corresponds to the highest Eigen value of the covariance matrix. Significance of the second, third, etc. principal components decrease as they correspond to less and less Eigen values in the diagonal matrix, D. By deleting insignificant principal components or by deleting those columns corresponding to low Eigen values of D, dimensionality reduction can be obtained and thereby insignificant data such as noise can be forced to zero or low values”)
and then projected see pg. 6, Section IV, “The PCA projection of the reshaped matrix is done by the matrix multiplication where A is the reshaped matrix and PC is the principal components”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the vector selection and projection taught by Malini with the image denoising method taught by Pan in view of Nobukuni and Zeng. The motivation for doing so would be to reduce dimensions by deleting insignificant. Thus, it would have been obvious to combine the eigen vector selection taught by Malini with the teachings of Pan and Nobukuni and Zeng in order to obtain the invention as claimed in Claim 8.
As to Claim 9, Pan in view of Nobukuni and Zeng fails to explicitly teach the at least one processor is further configured to obtain the second denoising image by selecting and projecting a number of eigen vectors set based on properties of the input image from among the plurality of eigen vectors.
However, Malini teaches a number of eigen vectors are selected based on image properties (see Section IV, pgs. 7-8, “For this, the noisy 2D image matrix of size M×N is reshaped to get a new matrix, , with four columns with each column of (M×N)/4 pixels. The pixels in each column of the new matrix are obtained by local 2×2 sub image chosen from the neighboring columns and neighboring rows of original image matrix. The principal component analysis is done on the new matrix. It results in the Eigen vectors or the principal components (PC) with 4 columns and (M×N)/4 rows”, where the number of eigen vectors is dependent on the size of the input image),
and then projected see pg. 6, Section IV, “The PCA projection of the reshaped matrix is done by the matrix multiplication where A is the reshaped matrix and PC is the principal components”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the vector selection and projection taught by Malini with the image denoising method taught by Pan in view of Nobukuni and Zeng. The motivation for doing so would be to reduce dimensions by deleting insignificant vectors (see pg. 5, Section III). Thus, it would have been obvious to combine the eigen vector selection taught by Malini with the teachings of Pan and Nobukuni and Zeng in order to obtain the invention as claimed in Claim 8.
As to Claim 17, Pan in view of Nobukuni and Zeng fails to explicitly teach determining a priority of the plurality of eigen vectors based on the plurality of eigen value; and obtaining the second denoising image by selecting and projecting at least one eigen vector from among the plurality of eigen vectors, based on the determined priority.
However, Malini teaches that a number of eigen vectors can be selected (see pg. 5, Section III, “The first principal component corresponds to the first column of which has highest significance as it corresponds to the highest Eigen value of the covariance matrix. Significance of the second, third, etc. principal components decrease as they correspond to less and less Eigen values in the diagonal matrix, D. By deleting insignificant principal components or by deleting those columns corresponding to low Eigen values of D, dimensionality reduction can be obtained and thereby insignificant data such as noise can be forced to zero or low values.”)
and then projected see pg. 6, Section IV, “The PCA projection of the reshaped matrix is done by the matrix multiplication where A is the reshaped matrix and PC is the principal components”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the vector selection and projection taught by Malini with the image denoising method taught by Pan in view of Nobukuni and Zeng. The motivation for doing so would be to reduce dimensions by deleting insignificant vectors (see pg. 5, Section III). Thus, it would have been obvious to combine the eigen vector selection taught by Malini with the teachings of Pan and Nobukuni and Zeng in order to obtain the invention as claimed in Claim 17.
As to Claim 18, Pan in view of Nobukuni and Zeng fails to teach obtaining the second denoising image by selecting and projecting a preset number of eigen vectors of the plurality of eigen vectors.
However, Malini teaches selecting a preset number of eigen vectors of the plurality of eigen vectors properties (see Section IV, pgs. 7-8, “For this, the noisy 2D image matrix of size M×N is reshaped to get a new matrix, , with four columns with each column of (M×N)/4 pixels. The pixels in each column of the new matrix are obtained by local 2×2 sub image chosen from the neighboring columns and neighboring rows of original image matrix. The principal component analysis is done on the new matrix. It results in the Eigen vectors or the principal components (PC) with 4 columns and (M×N)/4 rows”, where the preset number of eigen vectors is based on the dimension of the image),
and then projecting these eigen vectors (see pg. 6, Section IV, “The PCA projection of the reshaped matrix is done by the matrix multiplication where A is the reshaped matrix and PC is the principal components”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the vector selection and projection taught by Malini with the image denoising method taught by Pan in view of Nobukuni and Zeng . The motivation for doing so would be to reduce dimensions by deleting insignificant vectors (see pg. 5, Section III). Thus, it would have been obvious to combine the eigen vector selection taught by Malini with the teachings of Pan and Nobukuni in order to obtain the invention as claimed in Claim 18.
As to Claim 19, Pan in view of Nobukuni and Zeng fails to teach further comprising obtaining the second denoising image by selecting and projecting a number of eigen vectors set based on properties of the input image from among the plurality of eigen vectors.
However, Malini teaches that a number of eigen vectors can be selected (see Section IV, pgs. 7-8, “For this, the noisy 2D image matrix of size M×N is reshaped to get a new matrix, , with four columns with each column of (M×N)/4 pixels. The pixels in each column of the new matrix are obtained by local 2×2 sub image chosen from the neighboring columns and neighboring rows of original image matrix. The principal component analysis is done on the new matrix. It results in the Eigen vectors or the principal components (PC) with 4 columns and (M×N)/4 rows”, where the number of eigen vectors is dependent on the size of the input image),
and then projected see pg. 6, Section IV, “The PCA projection of the reshaped matrix is done by the matrix multiplication where A is the reshaped matrix and PC is the principal components.”)..
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the vector selection and projection taught by Malini with the image denoising method taught by Pan in view of Nobukuni and Zeng. The motivation for doing so would be to reduce dimensions by deleting insignificant. Thus, it would have been obvious to combine the eigen vector selection taught by Malini with the teachings of Pan and Nobukuni and Zeng in order to obtain the invention as claimed in Claim 19.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. (US Pub No 20210241421), hereinafter Pan in view of Nobukuni et al. (JP Pub No 2017129952A), hereinafter Nobukuni, and further in view of Liang et al. (CN114511450), hereinafter Liang.
As to Claim 10, Pan in view of Nobukuni fails to explicility teach that at least one processor is further configured to down-sample the second denoising image; and generate the output image by summing the first denoising image and the down- sampled second denoising image of each of the plurality of channels.
However, in an analogous art of image analysis, Liang teaches an image noise reduction method (see Abstract, “The invention relates to an image noise reduction method,”),
which comprises obtaining a first image, and then performing noise reduction and down-sampling on the image to obtain a noise reduced image (see paragraph [0006-0007], “Downsampling the first image to obtain at least one second image with higher blur than the first image… respectively carrying out noise reduction on the first image and at least one second image to obtain a noise-reduced image”),
and then summing the first image with the down-sampled image in order to obtain a final image (see paragraph [0008], “And fusing the first image after noise reduction with at least one second image to obtain a first image after noise reduction, wherein the resolution of the second image is lower than or equal to that of the first image”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the down-sampling taught by Liang. The motivation for doing so would be to improve image quality. Liang teaches in paragraph [0069], “When the first image and the second image are denoised, the noise reduction of different noise reduction degrees among the images can be performed according to different image contents, so that the noise reduction is performed through different noise reduction degrees in a layering manner among the images, so that the image details are kept as much as possible, and then the denoised first image and at least one denoised downsampled image are fused to obtain the denoised image. Compared with the method for directly carrying out one-time intensity noise reduction on the first image, the method can keep image details as much as possible and improve image quality.” Thus, it would have been obvious to combine the down-sampling taught by Liang with the teachings of Pan and Nobukuni in order to obtain the invention as claimed in Claim 10.
As to Claim 20, Claim 20 claims the same limitation claimed as Claim 10 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al. (US Pub No 20160098821) teaches a method for reducing noise in an image which comprises obtaining an image, performing a denoising operation to obtain a first denoising image, obtaining a first difference image representing the difference between the first denoising image and the first image, processing the first difference image to obtain a second denoising image, and then summing the first denoising image and second denoising image in order to generate an output image.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOUMYA THOMAS whose telephone number is (571)272-8639. The examiner can normally be reached M-F 8:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.T./Examiner, Art Unit 2664
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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