Method and apparatus for rapidly reconstructing super-resolution image

Patent metadata

Field	Value
Publication number	US-2017193635-A1
Application number	US-201415314104-A
Country	US
Kind code	A1
Filing date	May 28, 2014
Priority date	May 28, 2014
Publication date	Jul 6, 2017
Grant date	—

Abstract

A method and apparatus for rapidly reconstructing a super-resolution image. In the method and apparatus for rapidly reconstructing a super-resolution image provided in the present application, an original image is processed at least by means of iterative backward mapping based on a texture structural constraint during reconstruction of a super-resolution image of the original image, so as to enhance texture details of the image, thereby improving the high-frequency detail quality of the super-resolution image.

First claim

The invention claimed is:

1 . A method for reconstructing a super-resolution image, the method comprising: processing an original image at least using iterative back-projection based on texture-structure constraints to enhance textural details of the original image during a procedure of reconstructing a super-resolution image from the original image.

2 . The method of claim 1 , wherein the using the iterative back-projection based on the texture-structure constraints, comprises:
inputting the original image; performing the iterative back-projection based on the texture-structure constraints on the original image to obtain a first super-resolution image; extracting edge regions from the original image to generate an edge image; performing super-resolution image reconstruction on the edge image based on an edge region dictionary to obtain a second super-resolution image; wherein the edge region dictionary comprises low-resolution samples and high-resolution samples corresponding to the low-resolution samples; and synthesizing the first super-resolution image with the second super-resolution image to obtain a super-resolution image of the original image.

3 . The method of claim 2 , wherein when extracting the edge image comprising information of the edge regions from the original image, sharp-edge portions of the original image and transition-region portions within a pre-set area range of the sharp-edge portions are all extracted as the edge regions.

4 . The method of claim 3 , wherein after determination of the edge regions, the edge regions are performed with morphological processing.

5 . The method of any of claims 1 - 4 , wherein the texture-structure constraints comprise: in the original image, for the texture regions with large grayscale changes, increasing a coefficient for iteration-increment of high-frequency information; and for the texture regions with small grayscale changes, decreasing the coefficient for iteration-increment of high-frequency information.

6 . The method of claim 2 , wherein the performing the iterative back-projection based on the texture-structure constraints on the original image to obtain the first super-resolution image, comprises:
pre-processing the original image to obtain a preprocessed image; and performing iterative back-projection based on the texture-structure constraints to the preprocessed image to obtain the first super-resolution image.

7 . The method of claim 6 , wherein the preprocessing comprises bilateral filtering.

8 . The method of any of claims 2 - 4 , wherein the synthesizing the first super-resolution image with the second super-resolution image to obtain the super-resolution image of the original image comprises: performing mean-value calculation on the transition-region portions in the first super-resolution image and the second super-resolution image, and allowing mean values at centers of the grayscale distributions to overlap by mean-value correction to obtain the super-resolution image of the original image.

9 . The method of claim 8 , further comprising: after the mean-value correction, adjusting grayscale values of the transition-region portions by performing a preset number of iterative back-projection on the transition-region portions to obtain the super-resolution image of the original image.

10 . An apparatus for reconstructing a super-resolution image, the apparatus comprising:
A) an original image acquisition unit, which is configured to acquire an original image; and B) a super-resolution image reconstruction module, which is configured to perform iterative back-projection based on texture-structure constraints on the original image during a procedure of reconstructing a super-resolution image from the original image to enhance textural details of the original image.

11 . The apparatus of claim 10 , wherein the super-resolution image reconstruction module comprises:
a first super-resolution image reconstruction unit, which is configured to perform the iterative back-projection based on the texture-structure constraints on the original image to obtain a first super-resolution image; an edge-image extraction unit, which is configured to extract edge regions from the original image to generate an edge image; a second super-resolution image reconstruction unit, which is configured to perform super-resolution image reconstruction on the edge image based on an edge region dictionary to obtain a second super-resolution image; wherein the edge region dictionary comprises low-resolution samples and high-resolution samples corresponding to the low-resolution samples; and a synthesis unit, which is configured to synthesize the first super-resolution image with the second super-resolution image to obtain the super-resolution image of the original image.

12 . The apparatus of claim 11 , wherein when the edge regions are extracted from the original image by the edge-image extraction unit to generate the edge image, sharp-edge portions of the original image and transition-region portions within a pre-set area range of the sharp-edge portions are extracted by the edge-image extraction unit as the edge regions.

13 . The apparatus of claim 12 , wherein the edge-image extraction unit is further configured to perform morphological processing on the edge regions after extraction of the edge regions from the original image.

14 . The apparatus of any of claims 10 - 13 , wherein the texture-structure-based constraints comprises: in the original image, for the texture regions with large grayscale changes, increasing the coefficient for iteration-increment of high-frequency information; and for the texture regions with small grayscale changes, decreasing the coefficient for iteration-increment of high-frequency information.

15 . The apparatus of claim 11 , wherein when the iterative back-projection based on the texture-structure constraints is performed on the original image by first super-resolution image reconstruction unit to obtain the first super-resolution image, the original image is pre-processed by the first super-resolution image reconstruction unit to obtain the preprocessed image, and the iterative back-projection based on the texture-structure constraints is then performed on the preprocessed image to obtain the first super-resolution image.

16 . The apparatus of claim 15 , wherein when pre-processing the original image by the first super-resolution image reconstruction unit, bilateral filtering is adopted by the first super-resolution image reconstruction unit to preprocess the original image.

17 . The apparatus of any of claims 11 - 13 , wherein when the first super-resolution image is synthesized with the second super-resolution image by the synthesis unit to obtain the super-resolution image of the original image, mean-value calculation is performed on the transition-region portions in the first super-resolution image and the second super-resolution image, and mean values at centers of the grayscale distributions are overlapped by mean-value correction to obtain the super-resolution image of the original image.

18 . The apparatus of claim 17 , wherein the synthesis unit is also configured for mean-value correction; after the mean values at centers of the grayscale distributions are overlapped by the mean-value correction, grayscale values of the transition-region portions are adjusted by performing a preset number of iterative back-projection on the transition-region portions to obtain the super-resolution image of the original image.

Assignees

• Univ Peking Shenzhen Graduate School

Inventors

• Zhao Yang
• Wang Ronggang
• Wang Zhenyu
• Gao Wen
• Wang Wenmin
• Dong Shengfu
• Huang Tiejun
• Ma Siwei

Classifications

• G06T2207/20028

  Bilateral filtering · CPC title

• G06T5/50

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

• G06T2207/20221

  Image fusion; Image merging · CPC title

• G06T3/4053 Primary

  based on super-resolution, i.e. the output image resolution being higher than the sensor resolution · CPC title