Acquisition method of sparse coefficient vector for recovering and enhancing video image

Abstract

The invention discloses an acquisition method of a sparse coefficient vector for recovering and enhancing a video image; images are respectively selected from a natural image library and fitting images with distinctive edges for (discrete cosine transform) DCT conversion, mapped to a frequency domain space, and are initially clustered based on the general frequency domain characteristics of the images; then each cluster is further secondarily clustered based on the high-frequency information characteristics thereof; finally the first m main component variables of the obtained secondary cluster are extracted, and the sparse sub dictionary subDi_j of the cluster is obtained; all the sparse sub dictionary subDi_js form the final structural sparse dictionary; and consequently, an established two-stage structural sparse dictionary library is different from a traditional long and low-efficiency linear complete dictionary. The method can quickly and efficiently solve the sparse expression of an input image video signal, can acquire the accurate and effective sparse coefficient vector a~ by carrying out collaborate level sparse building to any image video signal, and has rather high anti-noise performance.

Description

technical field [0001] The invention belongs to the technical field of video image enhancement processing, and more specifically relates to a method for acquiring sparse coefficient vectors used for video image restoration and enhancement in image video enhancement processing. Background technique [0002] Due to the inherent defects or limitations of the image and video acquisition system itself, the digital image data collected from the real scene through the camera system is the result of various image quality degradation effects. That is to say, due to various reasons, there is an obvious gap in visual quality between the picture seen from the collected digital image and the real scene. The most typical ones are the blurring effect caused by the point spread function (PSF) of the camera, the downsampling effect caused by the resolution limitation of the camera CMOS or CCD sensor chip, and the noise blurring effect caused by the superposition of air and camera system nois...

AI Technical Summary

Problems solved by technology

[0017] However, expressing local slices of natural images with a PCA basis computed from integrated images has its own inherent drawbacks

Because various natural image slices and regular image deviations can only be modeled by the variance parameters in the Gaussian model, which greatly loses the sparse coefficient vector accuracy of expression

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