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Super-resolution reconstruction optimization recovery method for under-sampled degraded images

A technology for super-resolution reconstruction and image degradation, applied in the field of image processing, it can solve the problems of poor effect, no practical application, blurred image edges, etc. effect of influence

Inactive Publication Date: 2016-12-07
BEIHANG UNIV
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Problems solved by technology

[0003] In the 1960s, Harris and Goodman first proposed super-resolution reconstruction of a single image, but the effect was not good, and it was not practically applied.
In 1996, Schultz and Stevenson proposed an image super-resolution reconstruction method based on the maximum a posteriori probability MAP, which uses multiple images in the spatial domain to reconstruct super-resolution reconstruction. The algorithm can effectively introduce the prior experience constraints of the image, but for the reconstructed The edges of the image are blurred

Method used

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  • Super-resolution reconstruction optimization recovery method for under-sampled degraded images
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specific Embodiment

[0044] 1. Read a multi-frame undersampling and degraded image sequence, and establish an image degradation model of the multi-frame undersampling and degraded image sequence;

[0045] The image degradation model adopted in the present invention is as follows: figure 2 shown, its formula is as follows:

[0046] Y i =D i H i F i X+V i i=1,...,N

[0047] Among them, X represents the original high-resolution clear image, Y i represents the ith low-resolution degraded image, V i represents vectorized additive random noise, F i from the representation image Y i Relative image X motion matrix, H i represents the fuzzy matrix, D i represents the sampling matrix. The degradation model can also be written as:

[0048] Y 1 Y 2 . ...

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Abstract

The invention relates to a super-resolution reconstruction optimization recovery method for under-sampled degraded images, and belongs to the field of image processing. The method comprises the steps of reading out multi-frame under-sampled degraded image sequences, and establishing an image degradation model of the multi-frame under-sampled degraded image sequences; carrying out motion estimation of the multi-frame under-sampled degraded image sequences to obtain relative motion information among the multi-frame under-sampled degraded image sequences; according to the image degradation model and the relative motion information, carrying out super-resolution reconstruction of the multi-frame under-sampled degraded image sequences by using a maximum a posteriori probability (MAP) method to obtain a super-resolution reconstructed image; and for the super-resolution reconstructed image, recovering the image using a Lucy-Richardson (RL) filter deconvolution recovery method to obtain the recovered image. The method can recover a high resolution image and keep the image edge and image detail information.

Description

technical field [0001] The invention relates to a super-resolution reconstruction optimization and restoration method of under-sampling and degraded images, and belongs to the field of image processing. Background technique [0002] Super-resolution image reconstruction technology is dedicated to recovering high-frequency information of images, improving image resolution, and increasing the amount of image information. The research on high-resolution image reconstruction mainly includes the following: the first is to mathematically model the degradation process of the image, and to identify its main influencing factors and influence processes; then the research of image motion estimation algorithm, which needs to be accurate to the sub-pixel level; finally It is the research of image reconstruction algorithm, (retaining the details and texture of the image, suppressing the influence of noise, establishing an image reconstruction optimization decision model) is also the main ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40
CPCG06T3/4053
Inventor 秦世引高明
Owner BEIHANG UNIV
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