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 .
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(s) [1-2, 9, 11 and 14-16] is/are rejected under 35 U.S.C. 103 as being unpatentable over Han (US. 2023/0056564) in view of Hannigan (US. PAT. No. 6, 535,617).
Reclaim [1] Han discloses an operating method of an image processing device (see fig. 2), the operating method comprising: receiving at least one image group including a plurality of images (see 202 fig. 2 and ¶¶0048-0049, the image may alternatively be an image including other things, for example: an image showing a person, an object or landscapes, and any image whose authenticity is required to be detected can be used as the image);
Han doesn’t seem to exactly discloses detecting, from the at least one image group, fixed pattern noise (FPN) information using Fast Fourier Transform (FFT); and correcting FPN of an original image generated by an image sensor based on the FPN information.
Nonetheless in the same field of endeavor Hannigan discloses an image processing system as Han (see Hannigan fig. 6). Hanigan further discloses detecting, from the at least one image group, fixed pattern noise (FPN) information using Fast Fourier Transform (FFT) (col. 33-334 lines 67 -5, the method takes an FFT of the filtered data. The FFT spectrum is then normalized by the same normalization process as above. Then the fixed pattern noise is removed in the frequency domain in the same way as in the spatial domain [by the virtue of normalized FT spectrum gives an estimation of the fixed pater noise as for example disclosed in col. 33 lines 47-50]); and correcting FPN of an original image generated by an image sensor based on the FPN information (see col. 34 lines 25-29 and col. 33-334 lines 67 -5, the method takes an FFT of the filtered data. The FFT spectrum is then normalized by the same normalization process as above. Then the fixed pattern noise is removed in the frequency domain in the same way as in the spatial domain [by the virtue of removing the fixed pattern noise correction is achieved]).
Hence it would have been obvious to one of ordinary skill in the art to have been motivated to modify Han before the effective filling date of the claimed invention by the teachings of Hannigan since this would allow to detect fixed pattern noise using Fast Fourier Trans formation and remove the fixed pattern noise from an image data.
Reclaim [2] Han as modified further discloses, wherein the at least one image group comprises two or more image groups captured at different illuminances at regular intervals (see Hannigan, col. 33 lines 13-17Next, the camera is placed at random rotations (i.e. it could be upside down) so that variations in illumination of the subject to location of the light source are averaged out. The frames captured are then summed to create a composite image, [variation in illumination implies different illumination]).
Reclaim [9] Han as modified further discloses wherein the correcting the FPN based on the FPN information comprises: generating a virtual image based on the FPN information; and correcting the FPN by subtracting the virtual image from the original image (see Hannigan, col. 33 lines 47-59, . Now, for each frame, the method calculates the dot product of the frame and this normalized filtered composite image. This gives an estimation of the amount of fixed pattern noise similar to the composite image that is contained in a given image frame. The method then subtracts the composite image, multiplied by this dot product.
(208) Here's a mathematical description: ci=composite image, normalized id=image data fd=final data fd=id-dot(ci,id)*ci. This final data has substantially less fixed pattern noise [ the virtual image being dot(ci,id)*ci, the original image being, Id (image data)].
Reclaim [11], Han discloses an image processing device (see fig. 3), comprising: an image sensor configured to output image data (see ¶0065, where y.sub.ij is an image outputted by a camera sensor, x.sub.ij is incident light received by the camera sensor, f.sub.ij is a photo-response non-uniformity multiplicative noise factor, η.sub.ij is shot noise, c.sub.ij is dark current noise, and ε.sub.ij is additional random noise. i=1, . . . , m, j=1, . . . n, and m×n is the resolution of the camera sensor);
Han doesn’t seem to explicitly discloses an image signal processor configured to group the image data into at least one image group based on illuminance conditions, detect, based on the at least one image group, fixed pattern noise (FPN) information using Fast Fourier Transform (FFT), and correct the FPN based on the FPN information.
Nonetheless in the same field of endeavor Hannigan discloses an image processing system as Han (see Hannigan fig. 6). Hanigan further discloses an image signal processor configured to group the image data into at least one image group based on illuminance conditions (see col. 33, lines 4-8, This implementation begins by collecting a series of images from an image sensor. To begin, a user or an automated process collects a number of frames of video (200+) while the camera's CCD or CMOS sensor receives uniform illumination) , detect, based on the at least one image group, fixed pattern noise (FPN) information using Fast Fourier Transform (FFT) (col. 33-334 lines 67 -5, the method takes an FFT of the filtered data. The FFT spectrum is then normalized by the same normalization process as above. Then the fixed pattern noise is removed in the frequency domain in the same way as in the spatial domain [by the virtue of normalized FT spectrum gives an estimation of the fixed pater noise as for example disclosed in col. 33 lines 47-50]), and correct the FPN based on the FPN information (see col. 34 lines 25-29 and col. 33-334 lines 67 -5, the method takes an FFT of the filtered data. The FFT spectrum is then normalized by the same normalization process as above. Then the fixed pattern noise is removed in the frequency domain in the same way as in the spatial domain [by the virtue of removing the fixed pattern noise correction is achieved]).
Hence it would have been obvious to one of ordinary skill in the art to have been motivated to modify Han before the effective filling date of the claimed invention by the teachings of Hannigan since this would allow to detect fixed pattern noise using Fast Fourier Trans formation and remove the fixed pattern noise from an image data.
Reclaim [14] Han as modified further discloses , further comprising a memory for storing the FPN information, wherein the image signal processor is configured to: generate a virtual image based on the FPN information; and correct the FPN by subtracting the virtual image from an original image generated by the image sensor (see Hannigan, col. 33 lines 47-59, . Now, for each frame, the method calculates the dot product of the frame and this normalized filtered composite image. This gives an estimation of the amount of fixed pattern noise similar to the composite image that is contained in a given image frame. The method then subtracts the composite image, multiplied by this dot product.
(208) Here's a mathematical description: ci=composite image, normalized id=image data fd=final data fd=id-dot(ci,id)*ci. This final data has substantially less fixed pattern noise [ the virtual image being dot(ci,id)*ci, the original image being, Id (image data)]) .
Reclaim [15] Han as modified further discloses, wherein the memory stores the FPN information to correspond to a number of columns or rows (see Hannigan, 1308, fig. 21 and col. 33 lines 47-52, Now, for each frame, the method calculates the dot product of the frame and this normalized filtered composite image. This gives an estimation of the amount of fixed pattern noise similar to the composite image that is contained in a given image frame. The method then subtracts the composite image, multiplied by this dot product, [performing the dot product implies a matrix arrangement in the buffer a fixed pattern noise is estimated]). .
Reclaim [16] Han as modified further discloses, wherein the illuminance condition includes an illuminance of an interval (see Hannigan col. 33 lines 6-8, automated process collects a number of frames of video (200+) while the camera's CCD or CMOS sensor receives uniform illumination [illumination while collecting a number of frames]).
Allowable Subject Matter
Claims [17-20] are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Reclaim[17] none of the prior arts on the record either singularly or in combination teaches or reasonably suggests: An image processing system, comprising: generate a plurality of average images corresponding to each of the plurality of image groups, measure an amplitude and phase of each of the plurality of average images using Fast Fourier Transform (FFT), detect fixed pattern noise (FPN) information based on the amplitude and phase of each of the plurality of average images, correct FPN based on the FPN information; and a memory configured to store data therein; In conjunction with the other limitation of the claim.
Claims [18-20] are allowed due to their direct or indirect dependency on claim [17].
Claims [3-8, 10 and 12-13] objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter
Reclaim [3] none of the prior art on the record either singularly or in combination teaches or reasonably suggests: wherein the detecting the FPN information comprises: generating average images corresponding to the at least one image group based on average values of pixel values of the plurality of images included in each of the at least one image group; and detecting, from the average images, the FPN information using the FFT.
Claims [4-7] are allowed due to their direct or indirect dependency on claim [3].
Reclaim [8] none of the prior art on the record either singularly or in combination teaches or reasonably suggests: . The operating method of claim 1, wherein the FPN information comprises column FPN information and row FPN information, the detecting, from the at least one image group, the FPN information comprises generating a column average image based on average values for column line pixel values of the plurality of images; generating a row average image based on average values of row line pixel values of the plurality of images; and detecting, from the column average image and the row average image, the column FPN information and the row FPN information using the FFT, and the correcting the FPN based on the FPN information comprises correcting the FPN based on the column FPN information and the row FPN information.
Reclaim [10] none of the prior arts on the record either singularly or in combination teaches or reasonably suggests: The operating method of claim 1, wherein the FPN information comprises a period of primary FPN, a start point of the primary FPN, and an LSB slope of the primary FPN, and the detecting the FPN information comprises: measuring an amplitude of the at least one image group using the FFT; detecting a period of the FPN based on the amplitude of the at least one image group; determining, as the period of the primary FPN, a period of the FPN, that is less than or equal to a threshold period, in the period of the FPN; and detecting the start point of the primary FPN and the LSB slope of the primary FPN based on the period of the primary FPN.
Reclaim [12] none of the prior arts on the record either singularly or in combination teaches or reasonably suggests: The image processing device of claim 11, wherein the FPN information comprises a period of the FPN, a start point of the FPN, and a least significant bit (LSB) slope of the FPN.
Claim [13] is allowed due to its dependency on claim [12].
Conclusion
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/AHMED A BERHAN/Primary Examiner, Art Unit 2639