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Construction method of structural sparse dictionary for video image recovery enhancement

A technology of video image and construction method, which is applied in image enhancement, image analysis, image data processing, etc., can solve the problem of loss of accuracy of sparse expression, and achieve the effect of avoiding lengthy calculation, accurate sparse coefficient vector, and removing information redundancy

Inactive Publication Date: 2013-12-04
SICHUAN PANOVASIC TECH
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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 the deviation between various natural image slices and regular images can only be modeled by the variance parameter in the Gaussian model, which greatly loses the accuracy of sparse expression

Method used

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  • Construction method of structural sparse dictionary for video image recovery enhancement
  • Construction method of structural sparse dictionary for video image recovery enhancement
  • Construction method of structural sparse dictionary for video image recovery enhancement

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Embodiment Construction

[0035] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when detailed descriptions of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0036] figure 2 It is a specific flow chart of the construction method of the structured sparse dictionary used for video image restoration enhancement in the present invention.

[0037] In this implementation, if figure 2 Shown, the concrete implementation process of the present invention is as follows:

[0038] (1) Select images of a certain magnitude from the provided natural image library 201 and the provided fitted image library 202 with sharp edges, and the ratio of the two can be determined according to the situation, for example, 3:1. Extract the length and width...

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Abstract

The invention discloses a construction method of a structural sparse dictionary for video image recovery enhancement. The construction method comprises the following steps of: carrying out DCT (Discrete Cosine Transform) on image pieces respectively selected from a natural image library and a fitting image with a clear edge, mapping the image pieces to a radio frequency space, carrying out an initial clustering process based on characteristics of a normal frequency domain of the image piece, then carrying out a secondary flustering process on each cluster based on the high-frequency information characteristics of the cluster, finally extracting first m main element components from the obtained secondary cluster to obtain a sparse sub-dictionary subDi-j of the secondary cluster, combining all obtained sparse sub-dictionaries subDi-j into a final structural sparse dictionary The established two-level structural sparse dictionary library is different from a traditional tediously long low-efficiency linear over-complete dictionary, can be used for quickly and effectively solving sparse expression of input image video signals, sparse coefficient vectors obtained by carrying out synergic and hierarchical sparse modeling on any image video signals are exact and effective, and an anti-noise characteristic with a high degree is provided.

Description

technical field [0001] The invention belongs to the technical field of video image enhancement processing, and more specifically relates to a method for constructing a structured sparse dictionary 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 blurring effect caused by the point spread function (PSF) of the camera, downsampling effect caused by the resolution limit of the camera CMOS or CCD sensor chip, and noise blurring effect caused by the superposition of air and camera system noise. The ca...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T7/00
Inventor 袁梓瑾
Owner SICHUAN PANOVASIC TECH
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