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 .
Information Disclosure Statement
The information disclosure statement (IDS) filed on March 20, 2026, has been considered by the examiner. Applicant has listed the Office Action for Chinese Patent Application 202111266280 in the IDS filed on March 20, 206. However, an Office Action for the Chinese Patent Application 202311556616.4 (which is unrelated to the instant application) was uploaded instead. For this reason, the Examiner has crossed out this reference on the IDS.
Appropriate correction is required.
Response to Amendment
Applicants Amendments filed on June 11, 2026, have been entered and made of record.
Currently pending Claim(s): 1-8, 10-11, 13-20
Independent Claim(s): 1, 10, 11
Amended Claim(s): 1, 4, 5, 6, 8, 10, 11, 15, 20
Canceled Claim(s): 9, 12
Drawing Objections
In view of Applicant’s amendment to the Drawings, the previous objection is withdrawn.
Claim Rejections – 35 U.S.C 101
In view of Applicant’s amendment to Claim 11, the previous 101 rejection of Claim 11 is withdrawn.
Response to Arguments
This office action is responsive to the Applicant’s Arguments/Remarks Made in an Amendment
received on June 11, 2026.
In view of amendments filed on, the Applicant has amended independent Claim 1 to recite the additional limitation of, “wherein the aligning each of neighborhood features of the fusion feature with a target feature of the fusion feature to obtain an alignment feature corresponding to the RDB that outputs the fusion feature, comprises: upsampling the target video frame and each of the neighborhood video frames of the target video frame, to obtain an upsampled video frame of the target video frame and an upsampled video frame of each of the neighborhood video frames”. Originally, (in the claim set dated March 28, 2024) Claim 1 was rejected under Nah et al. (S. Nah, H. Dong, et al., "NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019), hereinafter Nah. Claim 4 was rejected over Nah in view of Gupta et al. (A. Gupta, et al., "Enhancing and experiencing spacetime resolution with videos and stills," 2009 IEEE International Conference on Computational Photography (ICCP)), hereinafter Gupta.
The Applicant argued that neither Nah nor Gupta teaches the newly added limitation in Claim 1. The Applicant explained (on Remarks pg. 14, paragraph 1) that Gupta only teaches upsampling a single low-resolution video frame, and comparing the low-resolution frame to high-resolution frames, and thus does not teach “upsampling the target video frame and each of the neighborhood video frames”.
The Examiner respectfully disagrees. Gupta teaches upsampling a target video frame and each of the neighborhood video frames (see Gupta, pg. 3, Section 3.1, “The input consists of a stream of low-resolution frames with intermittent high-resolution stills. We upsample the low resolution frames”, wherein the Examiner has interpreted the stream of frames as the target and neighborhood frames. Fig. 2, shown below, displays a neighborhood of low resolution frames represented by gray boxes, which are upsampled).
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Gupta, Fig. 2
Thus, the Examiner, for the reasons cited above, the Examiner has maintained the rejection of Claim 1 under Nah in view of Gupta. Therefore, the rejection of the dependent Claims 2-4 and 6-8 are also maintained. Claims 10 and 11, (previously rejected under Nah and Hu (CN 112565887)), which were amended to include the same limitation in Claim 1, are now rejected under Nah, Gupta, and Hu. Claims 5 and 16 are 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 Examiner thanks the Applicant for addressing the typographical error in the previous Office Action.
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.
Claims 1, 2-4, 6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Nah et al. (S. Nah et al., "NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019), hereinafter Nah, in view of Gupta et al. (A. Gupta, et al., "Enhancing and experiencing spacetime resolution with videos and stills," 2009 IEEE International Conference on Computational Photography (ICCP)), hereinafter Gupta.
As to Claim 1, Nah teaches a video super-resolution method, comprising (see pg. 1991, Section 4.7, “XJTU-IAIR team proposes a flow-guided spatio temporal dense network (FSTDN) for the joint video de blurring and super-resolution task as shown in Fig. 9.”, and see corresponding network shown in Fig. 9):
acquiring a first feature, wherein the first feature is a feature obtained by merging an initial feature of a target video frame and an initial feature of each of neighborhood video frames of the target video frame (see Fig. 9, where the 5D tensor is the first feature, formed by extracting features from target frame
L
R
t
and the neighborhood of frames
L
R
t
+
1
,
L
R
t
+
2
,
L
R
t
-
1
,
and
L
R
t
-
2
).
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423
655
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(Fig. 9 of Nah)
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358
757
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(Fig. 2 of Instant Application)
processing the first feature by concatenated multistage residual dense blocks (RDBs) (see Fig. 9, multiple residual dense blocks labeled 3D-RDB),
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423
510
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(Fig. 9 of Nah)
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362
680
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(Fig. 2 of Instant Application)
to obtain a fusion feature output by a RDB in each stage (see Fig. 9, see features output from 3D-RDBs, labelled
F
1
,
F
d
, and
F
D
);
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423
510
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(Fig. 9 of Nah)
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381
665
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(Fig. 2 of Instant Application)
for the fusion feature output by the RDB in each stage, aligning each of neighborhood features of the fusion feature with a target feature of the fusion feature to obtain an alignment feature corresponding to the RDB that outputs the fusion feature, (see Fig. 9, where
F
1
W
a
r
p
,
F
d
W
a
r
p
, and
F
D
W
a
r
p
are all alignment features corresponding to their respective 3D-RDB blocks, and see Feature Warping Layer of Fig. 9, where the neighborhood of features comprising the ‘fusion feature’
F
D
are warped by to a target feature)
PNG
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423
358
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(Fig. 9 of Nah)
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389
770
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(Fig. 2 of Instant Application)
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357
881
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(Fig. 9 of Nah)
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536
976
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(Fig. 4 of Instant Application)
wherein each of the neighborhood features of the fusion feature is a feature corresponding to each of the neighborhood video frames, and the target feature of the fusion feature is a feature corresponding to the target video frame (see Fig. 9, where the ‘fusion feature’
F
D
is split per frame, and the target feature is
F
d
,
t
corresponds to a feature of the target frame , and the neighborhood features
F
d
,
t
+
1
,
F
d
,
t
+
2
,
F
d
,
t
-
1
,
F
d
,
t
-
2
correspond to
L
R
t
+
1
,
L
R
t
+
2
,
L
R
t
-
1
,
L
R
t
-
2
respectively),
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357
885
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(Fig. 9 of Nah)
and generating a super-resolution video frame corresponding to the target video frame on the basis of the alignment feature corresponding to the RDB in each stage and the initial feature of the target video frame (see Fig 9., super resolution video frame
H
R
t
, generated from the alignment features and the initial feature
F
t
,
which is connected by the red dotted arrow).
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443
1431
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(Fig. 9 of Nah)
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391
793
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(Fig. 2 of Instant Application)
Nah fails to teach wherein the aligning each of neighborhood features of the fusion feature with a target feature of the fusion feature to obtain an alignment feature corresponding to the RDB that outputs the fusion feature, comprises: upsampling the target video frame and each of the neighborhood video frames of the target video frame, to obtain an upsampled video frame of the target video frame and an upsampled video frame of each of the neighborhood video frames.
However, in an analogous art Gupta teaches a video super-resolution method,
which comprises upsampling a neighborhood of video frames to obtain an upsampled video frame of the target video frame and an upsampled video frame of each of the neighborhood video frames (see Gupta, pg. 3, Section 3.1, “The input consists of a stream of low-resolution frames with intermittent high-resolution stills. We upsample the low resolution frames”, wherein the Examiner has interpreted the stream of frames as the target and neighborhood frames, and see pg. 2, Fig. 2, where many neighboring low resolution video frames are shown),
then calculating the flow between the upsampled frames (see page 3.1, “The system estimates motion between every fi and corresponding Sleft & Sright… One approach is to compute optical flow directly from the high-resolution stills, Sleft or Sright, to the upsampled frames fi”.)
and then aligning the frames on the basis of optical flow between the upsampled video frames see page 3, section 3.1, “Once the system has computed correspondences from Sleft to fi and Sright to fi, it warps the high-resolution stills to bring them into alignment with fi”).
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 upsampling taught by Gupta with the super-resolution method taught by Nah. The motivation for doing so would be to reduce errors in super-resolution.
Gupta teaches on page3, section 3.1, “The summed motion estimation serves as initialization to bring long range motion within the operating range of the optical flow algorithm and reduces the errors accumulated from the pairwise sums.” Thus, it would have been obvious to combine the teachings of Gupta with the teachings of Nah to obtain the invention as claimed in Claim 1.
As to Claim 2, Nah in view of Gupta teaches acquiring an optical flow between each of the neighborhood video frames and the target video frame respectively (see Nah, pg. 1991, Section 4.7, “XJTU-IAIR team proposes a flow-guided patio temporal dense network (FSTDN) for the joint video de blurring and super-resolution task as shown in Fig. 9.”, and see calculated flows
F
l
o
w
t
+
1
,
F
l
o
w
t
+
1
,
F
l
o
w
t
+
1
,
F
l
o
w
t
+
1
, which represent the optical flow between the target frame
L
R
t
and each respective neighboring frame)
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357
815
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(Fig. 9 of Nah)
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493
716
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(Fig. 2 of Instant Application)
and aligning each of neighborhood features of the fusion feature with a target feature of the fusion feature on the basis of the optical flow between each of the neighborhood video frames and the target video frame (see Nah, Fig. 9, ‘Feature Warping Layer’, where each feature fusion feature
F
d
is warped (aligned) using the flow calculated from the neighboring frames and target frames),
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357
881
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(Fig. 9 of Nah)
to obtain an alignment feature corresponding to the RDB that outputs the alignment feature (see
F
d
W
a
r
p
is generated for its respective 3D-RBD D block).
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423
358
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(Fig. 9 of Nah)
As to Claim 3, Nah in view of Gupta teaches splitting the fusion feature to obtain each of the neighborhood features and the target feature (see Fig.9, ‘Feature Warping Layer’, where the ‘fusion feature
F
d
is split to obtain target feature
F
d
,
t
and the neighboring features
F
d
,
t
+
1
,
F
d
,
t
+
2
,
F
d
,
t
-
1
,
F
d
,
t
-
2
),
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357
885
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(Fig. 9 of Nah)
aligning each of the neighborhood features with the target feature on the basis of the optical flow between each of the neighborhood video frames and the target video frame, to obtain an alignment feature for each of the neighborhood video frames (see Nah, Fig. 9, ‘Feature Warping Layer’, where each feature fusion feature of
F
d
(
F
d
,
t
+
1
,
F
d
,
t
+
2
,
,
F
d
,
t
,
F
d
,
t
-
1
,
F
d
,
t
-
2
) is warped (or aligned) using the flow calculated from the neighboring frames and target frames);
and merging the target feature and the alignment feature of each of the neighborhood video frames to obtain an alignment feature corresponding to the RDB that outputs the fusion feature (see Nah, Fig.9, where the warped features of the fusion features are concatenated to form
F
d
W
a
r
p
).
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357
850
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(Fig. 9 of Nah)
As to Claim 4, Nah fails to explicitly teach acquiring an optical flow between the upsampled video frame of each of the neighborhood video frames and the upsampled video frame of the target video frame; and aligning each of the neighborhood features of the fusion feature with the target feature of the fusion feature on the basis of the optical flow between the upsampled video frame of each of the neighborhood video frames and the upsampled video frame of the target video frame, to obtain an alignment feature corresponding to the RDB that outputs the fusion feature.
However, Gupta teaches a method for enhancing the spacetime resolution of videos (see abstract on page 1), which includes upsampling adjacent video frames (see page 3, section 3.1, “The input consists of a stream of low-resolution frames with intermittent high-resolution stills. We upsample the low-resolution frames using bicubic”),
then calculating the flow between the upsampled frames (see page 3.1, “The system estimates motion between every fi and corresponding Sleft & Sright… One approach is to compute optical flow directly from the high-resolution stills, Sleft or Sright, to the upsampled frames fi”.)
and then aligning the frames on the basis of optical flow between the upsampled video frames see page 3, section 3.1, “Once the system has computed correspondences from Sleft to fi and Sright to fi, it warps the high-resolution stills to bring them into alignment with fi”).
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 upsampling taught by Gupta with the super-resolution method taught by Nah. The motivation for doing so would be to reduce errors in super-resolution. Gupta teaches on page3, section 3.1, “The summed motion estimation serves as initialization to bring long range motion within the operating range of the optical flow algorithm and reduces the errors accumulated from the pairwise sums.” Thus, it would have been obvious to combine the teachings of Gupta with the teachings of Nah to obtain the invention as claimed in Claim 4.
As to Claim 6, Nah in view of Gupta teaches that generating a super-resolution video frame corresponding to the target video frame on the basis of the alignment feature corresponding to the RDB in each stage and the initial feature of the target video frame, comprises: merging alignment features corresponding to the multistage RDBs to obtain a second feature (see Nah, Fig.9, the alignment features
F
1
W
a
r
p
,
F
d
W
a
r
p
, and
F
D
W
a
r
p
being concatenated to form a 5D tensor),
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522
886
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(Fig. 9 of Nah)
and converting, based on a feature conversion network, the second feature into a feature having the same tensor as an initial feature of the target video frame to obtain a third feature (see Nah, Fig. 9, ‘Temporal Fusion’, and see how the initial 5D Tensor (with dimensions n*(64*D)*5*h*w) is converted to a 4D tensor (with dimensions n*64*h*w). Additionally, see how the initial feature of the target frame is summed with the fourth feature, thus implying that the third feature is the same dimensions as the initial feature),
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485
1393
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(Fig. 9 of Nah)
and generating a super-resolution video frame corresponding to the target video frame on the basis of the third feature and the initial feature of the target video frame (see Nah, Fig. 9, super resolution video frame
H
R
t
, generated from the 4D tensor and the initial feature
F
t
,
which is connected by the red dotted arrow).
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443
1431
media_image20.png
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(Fig. 9 of Nah)
As to Claim 8, Nah in view of Gupta teaches performing summation fusion on the third feature and the initial feature of the target video frame to obtain a fourth feature (see Nah, Fig.9, where the 4D tensor is the ‘third feature’ and the initial feature is added as indicated by the summation sign to obtain the fourth feature)
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485
1316
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(Fig. 9 of Nah)
processing the fourth feature by a residual dense network RDN to obtain a fifth feature (see Nah, Fig 9, where the fourth feature is input into a 2D RDN to obtain the fifth feature);
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485
623
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(Fig. 9 of Nah)
and upsampling the fifth feature to obtain a super-resolution video frame corresponding to the target video frame (see the upsampling block after the 2D RDN, which then outputs the super-resolution frame
H
R
t
).
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485
785
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(Fig. 9 of Nah)
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331
827
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(Fig. 4 of Instant Application)
Claims 7, 10, 13-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nah et al. (S. Nah et al., "NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results,"2019), hereinafter Nah, in view of Gupta et al. (A. Gupta, et al., "Enhancing and experiencing spacetime resolution with videos and stills," 2009 IEEE International Conference on Computational Photography (ICCP)), hereinafter Gupta, and further in view of Hu et al. (CN 112565887), hereinafter Hu.
As to Claim 7, Nah in view of Gupta, teaches, the feature conversion network comprises a first convolutional layer, a second convolutional layer, and a third convolutional layer concatenated sequentially; and the second convolutional layer and the third convolutional layer both have a kernel of 3*3*3 and have a padding parameter of 0 in a time dimension and a padding parameter of 1 in both length dimension and width dimension (see Nah, Fig.9, ‘Temporal Fusion ‘ block with three convolutional layers, and see kernel and padding labeled for the second and third convolutional layer, where ‘k’ stands for kernel, and ‘pad’ stands for padding).
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492
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(Fig.9 of Nah, with kernel and padding size)
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277
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(Fig.5 of instant application, with kernel and padding sizes)
Nah and Gupta fail to explicitly teach that the first convolutional layer has a kernel of 1 *1* 1 and has a padding parameter of 0 in each dimension.
However, Hu teaches a super-resolution method, which includes a pointwise convolution kernel (see paragraph [0102], “This application introduces the depthwise separable convolution in the neural network model. The depthwise separable convolution uses different convolution kernels for each channel of the input image for operation and operation, and the operation steps can be divided into depthwise convolution (Depthwise) Convolution with point (Pointwise)”, and see paragraph [0104], “The convolution kernel of deep convolution is k×k, the channel is cd, and the convolution kernel of point convolution is 1 ×1”, where it is known in the art that a pointwise kernel has no padding).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the convolutional kernel taught by Hu with the super-resolution method taught by Nah and Gupta. The motivation for doing so would be to reduce the amount of calculation needed (see paragraph [0104] and [0106], “Further, the depth separable convolution is to split the one-step convolution operation into two steps of deep convolution and point convolution…Compared with the standard convolution, the amount of calculation is reduced”). Thus, it would have been obvious to combine the kernel taught by Hu with the teachings of Nah in order to obtain the invention as claimed in Claim 7.
As to Claim 10, Claim 10 is directed towards an electronic device, comprising a memory and a processor, the memory being configured to store a computer program, the processor being configured to, when executing the computer program, cause the electronic device to implement the same method as claimed in Claim 1.
Nah in view of Gupta teaches the video super-resolution method of Claim 1, but fails to explicitly teach an electronic device comprising a memory and a processor.
However, Hu teaches a video super-resolution device (see paragraph [0001], “The embodiments of the present invention provide a video processing method, device, terminal, and storage medium, which can adaptively adjust a super-resolution strategy to perform super-resolution reconstruction on a video stream, thereby effectively improving video quality”),
which comprises a memory and processor (see paragraph [0060], “In another aspect, an embodiment of the present invention provides an intelligent terminal, which includes a processor, a communication interface, and a memory”).
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 video super-resolution device taught by Hu with the video super resolution method that by Nah and Gupta. The motivation for doing so would be to integrate the device into another system. Hu teaches in paragraph [0077], “The video processing system may be specifically integrated in an electronic device, and the electronic device may be a terminal or a server. For example, the video processing system can be integrated in the terminal. The terminal may be a mobile phone, a tablet computer, a notebook computer, a desktop computer, a personal computer (PC, Personal Computer), a TV, or other smart playback device, which is not limited in this application.” Thus, it would have been obvious to combine the video-super resolution device taught by Hu with the method taught by Nah and Gupta to obtain the invention as claimed in Claim 10.
As to Claim 11, Claim 11 is directed towards a non-transitory computer-readable storage medium, the computer-readable storage medium storing a computer program which, when executed by a computing device, causing the computing device to implement the same method as claimed in Claim 1.
Nah in view of Gupta teaches the video super-resolution method of Claim 1, but fails to explicitly teach a non-transitory computer-readable storage medium.
However, Hu teaches a computer-readable storage medium (see paragraph [0001], “The embodiments of the present invention provide a video processing method, device, terminal, and storage medium, which can adaptively adjust a super-resolution strategy to perform super-resolution reconstruction on a video stream, thereby effectively improving video quality”),
which can contain a computer program (see paragraph [0060], “The processor, the communication interface, and the memory are connected to each other, wherein the memory is used to store a computer program, The computer program includes program instructions, and the processor is configured to call the program instructions for performing operations involved in the foregoing video processing method”).
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the video processing device taught by Hu with the video processing method taught by Nah and Gupta. The motivation for doing so would be to integrate the device into other electronic devices, as taught by Hu in paragraph [0077]. Thus, it would have been obvious to combine the video-super resolution device taught by Hu with the super-resolution method taught by Nah and Gupta to obtain the invention as claimed in Claim 11.
As to Claim 13, Claim 13 claims the same limitation as Claim 2 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 2.
As to Claim 14, Claim 14 claims the same limitation as Claim 3 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 3.
As to Claim 15, Nah and Hu fail to explicitly acquiring an optical flow between the upsampled video frame of each of the neighborhood video frames and the upsampled video frame of the target video frame; and aligning each of the neighborhood features of the fusion feature with the target feature of the fusion feature on the basis of the optical flow between the upsampled video frame of each of the neighborhood video frames and the upsampled video frame of the target video frame, to obtain an alignment feature corresponding to the RDB that outputs the fusion feature.
However, in an analogous art, Gupta teaches a method for enhancing the spacetime resolution of videos (see abstract on page 1), which includes upsampling adjacent video frames (see page 3, section 3.1, “The input consists of a stream of low-resolution frames with intermittent high-resolution stills. We upsample the low-resolution frames using bicubic interpolation”),
then calculating the flow between the upsampled frames (see page 3.1, “The system estimates motion between every fi and corresponding Sleft & Sright… One approach is to compute optical flow directly from the high-resolution stills, Sleft or Sright, to the upsampled frames fi”.)
and then aligning the frames on the basis of optical flow between the upsampled video frames see page 3, section 3.1, “Once the system has computed correspondences from Sleft to fi and Sright to fi, it warps the high-resolution stills to bring them into alignment with fi”).
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 upsampling taught by Gupta with the super-resolution method taught by Nah and Hu. The motivation for doing so would be to reduce errors in super-resolution. Gupta teaches on page 3, section 3.1, “The summed motion estimation serves as initialization to bring long range motion within the operating range of the optical flow algorithm and reduces the errors accumulated from the pairwise sums.” Thus, it would have been obvious to combine the upsampling taught by Gupta with the teachings of Nah and Hu in order to obtain the invention as claimed in Claim 15.
As to Claim 17, Claim 17 claims the same limitation as Claim 6 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 6.
As to Claim 18, Claim 18 claims the same limitation as Claim 7 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 7.
As to Claim 19, Claim 19 claims the same limitation as Claim 6 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 8.
As to Claim 20, Claim 20 claims the same limitation as Claim 2 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 2.
Allowable Subject Matter
Claims 5 and 16 are 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.
Nah, Gupta, and Hu fail to teach: upsampling each of the neighborhood features and the target feature respectively, to obtain an upsampled feature of each of the neighborhood video frames and an upsampled feature of the target video frame; aligning the upsampled feature of each of the neighborhood video frames with the upsampled feature of the target video frame on the basis of the optical flow between the upsampled video frame of each of the neighborhood video frames and the upsampled video frame of the target video frame, to obtain an upsampled alignment feature of each of the neighborhood video frames; performing a space-to-depth conversion on the upsampled feature of the target video frame and the upsampled aligned feature of each of the neighborhood video frames respectively, to obtain an equivalent feature of the target video frame and an equivalent feature of each of the neighborhood video frames; and merging the equivalent feature of the target video frame and the equivalent feature of each of the neighborhood video frames, to obtain an alignment feature corresponding to the RDB that outputs the fusion features.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Abati et al. (US Pub No 20220301311), teaches a method of super-resolution which comprises upsampling a target image frame and neighborhood frames by aligning features corresponding to each frame.
Tang et al. (US Pub No 20210241470) teaches a video super-resolution method which comprises up-sampling image feature data after each feature is aligned.
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.
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/S.T./Examiner, Art Unit 2664
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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