Image enhancement method and apparatus

Patent metadata

Field	Value
Publication number	US-2022188999-A1
Application number	US-202217687374-A
Country	US
Kind code	A1
Filing date	Mar 4, 2022
Priority date	Sep 4, 2019
Publication date	Jun 16, 2022
Grant date	—

Abstract

This application relates to an image enhancement technology in the field of computer vision in the field of artificial intelligence, and provides an image enhancement method and apparatus. This application relates to the field of artificial intelligence, and specifically, to the field of computer vision. The method includes: adjusting a pixel value of a to-be-processed image, to obtain K images, where pixel values of the K images are different, and K is a positive integer greater than 1; extracting local features of the K images; extracting a global feature of the to-be-processed image; and performing image enhancement processing on the to-be-processed image based on the global feature and the local features, to obtain an image-enhanced output image. This method helps to improve the effect of image quality enhancement processing.

First claim

1 . An image enhancement method, comprising:
adjusting a pixel value of a to-be-processed image, to obtain K images, wherein pixel values of the K images are different, and K is a positive integer greater than 1; extracting local features of the K images; extracting a global feature of the to-be-processed image; and performing image enhancement processing on the to-be-processed image based on the global feature and the local features, to obtain an image-enhanced output image.

2 . The method according to claim 1 , wherein the adjusting the pixel value of the to-be-processed image, to obtain the K images comprises:
increasing all pixel values and/or decreasing all pixel values of the to-be-processed image through one or more non-linear transformations, to obtain the K images.

3 . The method according to claim 1 , further comprising:
performing semantic segmentation on the to-be-processed image, to obtain a semantic segmentation graph; and performing feature extraction on the semantic segmentation graph, to obtain a semantic condition, wherein the semantic condition comprises semantic information of the to-be-processed image, and at least one of the local features of the K images is extracted based on the semantic condition.

4 . The method according to claim 3 , wherein at least one of the local features of the K images is extracted based on a first semantic feature and a second semantic feature, wherein the first semantic feature and the second semantic feature are determined based on the semantic condition.

5 . The method according to claim 4 , wherein at least one of the local features of the K images is determined based on a residual estimated value, wherein the residual estimated value is determined based on the first semantic feature, the second semantic feature, and an image feature of the to-be-processed image.

6 . The method according to claim 1 , further comprising:
performing feature fusion on the local features of the K images, to obtain a local fusion feature, wherein the performing image enhancement processing on the to-be-processed image based on the global feature and the local features, to obtain the image-enhanced output image comprises:
performing image enhancement processing on the to-be-processed image based on the global feature and the local fusion feature, to obtain the image-enhanced output image.

7 . The method according to claim 1 , wherein the performing image enhancement processing on the to-be-processed image based on the global feature and the local features, to obtain the image-enhanced output image comprises:
performing feature fusion on the global feature and the local features, to obtain a fusion feature; and performing image enhancement processing on the to-be-processed image based on the fusion feature, to obtain the image-enhanced output image.

8 . The method according to claim 7 , wherein the performing feature fusion on the global feature and the local features, to obtain the fusion feature comprises:
performing feature extraction on the global feature, to obtain a first global feature and a second global feature; performing addition on the first global feature and the local features, to obtain a candidate fusion feature; and performing concatenation and convolution on the candidate fusion feature and the second global feature, to obtain the fusion feature.

9 . The method according to claim 7 , wherein the performing feature fusion on the global feature and the local features, to obtain the fusion feature comprises:
performing feature extraction on the global feature, to obtain a first global feature and a second global feature; performing concatenation and convolution on the first global feature and the local features, to obtain a candidate fusion feature; and performing addition on the candidate fusion feature and the second global feature, to obtain the fusion feature.

10 . The method according to claim 1 ,
wherein the to-be-processed image is an image obtained after an original image of the to-be-processed image is downsampled, wherein the performing image enhancement processing on the to-be-processed image based on the global feature and the local features, to obtain the image-enhanced output image comprises:
performing image enhancement processing on the original image of the to-be-processed image based on the global feature and the local features, to obtain the image-enhanced output image.

11 . An image enhancement apparatus, comprising:
a processor; and a memory, wherein the memory is configured to store program instructions, and the processor is configured to invoke the program instructions to perform a method comprising:
adjusting a pixel value of a to-be-processed image, to obtain K images, wherein pixel values of the K images are different, and K is a positive integer greater than 1;
extracting local features of the K images;
extracting a global feature of the to-be-processed image; and
performing image enhancement processing on the to-be-processed image based on the global feature and the local features, to obtain an image-enhanced output image.

12 . The apparatus according to claim 11 , wherein the adjusting the pixel value of the to-be-processed image, to obtain the K images comprises:
increasing all pixel values and/or decreasing all pixel values of the to-be-processed image through one or more non-linear transformations, to obtain the K images.

13 . The apparatus according to claim 11 , wherein the method further comprises:
performing semantic segmentation on the to-be-processed image, to obtain a semantic segmentation graph; and performing feature extraction on the semantic segmentation graph, to obtain a semantic condition, wherein the semantic condition comprises semantic information of the to-be-processed image, and at least one of the local features of the K images is extracted based on the semantic condition.

14 . The apparatus according to claim 13 , wherein at least one of the local features of the K images is extracted based on a first semantic feature and a second semantic feature, wherein the first semantic feature and the second semantic feature are determined based on the semantic condition.

15 . The apparatus according to claim 14 , wherein at least one of the local features of the K images is determined based on a residual estimated value, wherein the residual estimated value is determined based on the first semantic feature, the second semantic feature, and an image feature of the to-be-processed image.

16 . The apparatus according to claim 11 , wherein the method further comprises:
performing feature fusion on the local features of the K images, to obtain a local fusion feature, wherein the performing image enhancement processing on the to-be-processed image based on the global feature and the local features, to obtain the image-enhanced output image comprises:
performing image enhancement processing on the to-be-processed image based on the global feature and the local fusion feature, to obtain the image-enhanced output image.

17 . The apparatus according to claim 11 , wherein the performing image enhancement processing on the to-be-processed image based on the global feature and the local features, to obtain the image-enhanced output image comprises:
performing feature fusion on the global feature and the local features, to obtain a fusion feature; and performing image enhancement processing on the to-be-processed image based on the fusion feature, to obtain the image-enhanced output image.

18 . The apparatus

Assignees

• Huawei Tech Co Ltd

Inventors

• Wang Tao
• Wang Xian
• Song Fenglong

Classifications

• G06V10/26

  Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion · CPC title

• G06V10/30

  Noise filtering · CPC title

• G06V10/82

  using neural networks · CPC title

• G06V10/454

  Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN] · CPC title

• G06T5/50 Primary

  using two or more images, e.g. averaging or subtraction · CPC title