# US 2026/0073476 A1 · Image super-resolution reconstructing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0073476 A1 - Document ID: US20260073476A1 - Status: Published application - Kind: A1 - Application number: 19/394,557 - Filed: 2025-11-19 - Published: 2026-03-12 - Earliest claimed: 2020-06-30 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC - Inventors: Shuxin Zheng; Chang Liu; Di He; Guolin Ke; Jiang Bian; Tie-Yan Liu - Web page: https://patentgrep.com/patent/US20260073476A1 ## CPC - G06T 3/4053: based on super-resolution, i.e. the output image resolution being higher than the sensor resolution - G06T 3/4046: using neural networks - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract According to implementations of the subject matter described herein, a solution is proposed for super-resolution image reconstructing. According to the solution, an input image with first resolution is obtained. An invertible neural network is trained using the input image, wherein the invertible neural network is configured to generate an intermediate image with second resolution and first high-frequency information based on the input image, the second resolution being lower than the first resolution. Subsequently, an output image with third resolution is generated based on the input image and second high-frequency information by using an inverse network of the trained invertible neural network, the second high-frequency information conforming to a predetermined distribution, and the third resolution being higher than the first resolution. The solution can effectively process a low-resolution image obtained by an unknown downsampling method, thereby obtaining a high-quality and high-resolution image. ## Claims ### Claim 1 A computer-implemented method, comprising: obtaining an input image of a first resolution; training an invertible neural network with the input image, the invertible neural network being configured to generate an intermediate image of a second resolution that is lower than the first resolution, wherein training an invertible neural network with the input image includes determining a plurality of target functions based on the input image and the intermediate image; and generating, using an inverse network of the trained invertible neural network, an output image of a third resolution based on the input image and the intermediate image, the third resolution being greater than the first resolution. ### Claim 2 (depends on 1) The method of claim 1, wherein determining a plurality of target functions based on the input image and the intermediate image includes determining a first target function based on a difference between a pixel distribution in the intermediate image and a pixel distribution in an image block of the input image, the image block being of the second resolution. ### Claim 3 (depends on 2) The method of claim 2, wherein determining the first target function includes using a discriminator to distinguish whether a pixel of the intermediate image belongs to the intermediate image or the image block. ### Claim 4 (depends on 2) The method of claim 2, wherein the invertible neural network is further configured to generate the intermediate image using first high-frequency information based on the input image. ### Claim 5 (depends on 4) The method of claim 4, wherein generating, using an inverse network of the trained invertible neural network, an output image of a third resolution based on the input image and the intermediate image further includes generating the output image based on second high-frequency information conforming to a predetermined distribution and the input image. ### Claim 6 (depends on 5) The method of claim 5, wherein determining a plurality of target functions includes determining a second target function based on a difference between a distribution of the first high-frequency information and the predetermined distribution. ### Claim 7 (depends on 6) The method of claim 6, wherein determining a plurality of target functions includes generating, by using an inverse network of the invertible neural network, a reconstructed image of the first resolution based on third high-frequency information conforming to the predetermined distribution and the intermediate image. ### Claim 8 (depends on 7) The method of claim 7, wherein determining a plurality of target functions includes determining a third target function based on a difference between the input image and the reconstructed image. ### Claim 9 (depends on 8) The method of claim 8, wherein determining the plurality of target functions comprises: obtaining a reference image corresponding to semantics of the input image, the reference image being of the second resolution; and determining a fourth target function based on a difference between the intermediate image and the reference image. ### Claim 10 (depends on 1) The method of claim 1, wherein training an invertible neural network with the input image further comprises: determining a total target function for training the invertible neural network by combining at least some of the plurality of target functions; and determining network parameters for the invertible neural network by minimizing the total target function. ### Claim 11 (depends on 5) The method of claim 5, wherein the invertible neural network comprises a transforming module and at least one invertible network unit, and wherein generating the output image comprises: generating, based on the input image and the second high-frequency information and by using the at least one invertible network unit, a low-frequency component and a high-frequency component to be merged, the low-frequency component representing semantics of the input image and the high-frequency component being related to the semantics; and merging, by using the transforming module, the low-frequency component and the high-frequency component into the output image. ### Claim 12 (depends on 11) The method of claim 11, wherein the transforming module comprises at least one of an invertible convolution block and a wavelet transforming module. ### Claim 13 A device, comprising: a processing unit; and a memory coupled to the processing unit and comprising instructions stored thereon which, when executed by the processing unit, cause the device to perform acts comprising: obtaining an input image of a first resolution; training an invertible neural network with the input image, the invertible neural network being configured to generate an intermediate image of a second resolution that is lower than the first resolution, wherein training an invertible neural network with the input image includes determining a plurality of target functions based on the input image and the intermediate image; and generating, using an inverse network of the trained invertible neural network, an output image of a third resolution based on the input image and the intermediate image, the third resolution being greater than the first resolution. ### Claim 14 (depends on 13) The device of claim 13, wherein determining a plurality of target functions based on the input image and the intermediate image includes determining a first target function based on a difference between a pixel distribution in the intermediate image and a pixel distribution in an image block of the input image, the image block being of the second resolution. ### Claim 15 (depends on 14) The device of claim 14, wherein the invertible neural network is further configured to generate the intermediate image using first high-frequency information based on the input image. ### Claim 16 (depends on 15) The device of claim 15, wherein generating, using an inverse network of the trained invertible neural network, an output image of a third resolution based on the input image and the intermediate image further includes generating the output image based on second high-frequency information conforming to a predetermined distribution and the input image. ### Claim 17 (depends on 16) The method of claim 16, wherein determining a plurality of target functions includes determining a second target function based on a difference between a distribution of the first high-frequency information and the predetermined distribution. ### Claim 18 A computer program product being tangibly stored in a computer storage medium and comprising machine-executable instructions which, when executed by a device, cause the device to perform acts comprising: obtaining an input image of a first resolution; training an invertible neural network with the input image, the invertible neural network being configured to generate an intermediate image of a second resolution and first high-frequency information based on the input image, the second resolution being lower than the first resolution; and generating, using an inverse network of the trained invertible neural network, an output image of a third resolution based on second high-frequency information conforming to a predetermined distribution and the input image, the third resolution being greater than the first resolution. ### Claim 19 (depends on 18) The computer program product of claim 18, wherein the invertible neural network is further configured to generate the intermediate image using first high-frequency information based on the input image, and wherein generating, using an inverse network of the trained invertible neural network, an output image of a third resolution based on the input image and the intermediate image further includes generating the output image based on second high-frequency information conforming to a predetermined distribution and the input image, and wherein determining a plurality of target functions includes determining a second target function based on a difference between a distribution of the first high-frequency information and the predetermined distribution ### Claim 20 (depends on 18) The computer program product of claim 18, wherein training an invertible neural network with the input image further comprises: determining a total target function for training the invertible neural network by combining at least some of the plurality of target functions; and determining network parameters for the invertible neural network by minimizing the total target function.