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Image decompression method combining prior model and detail enhancement

A joint prior and decompression technology, applied in the field of image processing, can solve the problems that ordinary blocks cannot effectively separate noise and signal, blur edges, etc.

Active Publication Date: 2020-10-23
SICHUAN UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional low-rank minimization methods can remove block effects to a certain extent, but the ordinary squares they extract cannot effectively separate noise and signal, and are prone to blurred edges, and there is still room for improvement in enhancing image details

Method used

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  • Image decompression method combining prior model and detail enhancement
  • Image decompression method combining prior model and detail enhancement
  • Image decompression method combining prior model and detail enhancement

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Embodiment Construction

[0014] An image decompression method that combines prior model and detail enhancement mainly includes the following steps:

[0015] (1) For JPEG compressed images, a Gaussian quantization noise model is established to construct data items;

[0016] (2) Constructing a deblocking model based on a shape-adaptive low-rank prior and a quantization-constrained prior;

[0017] (3) Constructing a detail enhancement method based on sparse representation;

[0018] (4) Under the maximum a posteriori framework, establish the optimization function of the image decompression of joint prior model and details enhancement of the present invention;

[0019] (5) Use singular value threshold and convex quadratic minimization to solve the optimization function, and reconstruct the decompressed image.

[0020] Specifically, the Gaussian quantization noise model in the step (1) is:

[0021]

[0022] Among them, y represents the compressed image, x represents the original image, e represents ...

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Abstract

The invention discloses an image decompression method combining a prior model and detail enhancement. The method mainly comprises the following steps: for a JPEG compressed image, establishing a Gaussian quantization noise model to construct a data item; constructing a deblocking model based on the shape adaptive low-rank prior and the quantization constraint prior; constructing a detail enhancement method based on sparse representation; under a maximum posteriori framework, establishing an optimization function of image decompression combining a prior model and detail enhancement; and solvingthe optimization function by using a singular value threshold and convex quadratic minimization, and reconstructing a decompressed image. An image reconstructed by the image decompression method provided by the invention not only removes visually unpleasant block effect, but also retains rich detail information and has excellent performance on objective evaluation parameters. The method providedby the invention is an effective image decompression method.

Description

technical field [0001] The invention relates to an image decompression method for joint prior model and detail enhancement, which belongs to the field of image processing. Background technique [0002] JPEG is the most commonly used image compression standard, which can obtain the best image effect while reducing the amount of transmitted data. However, due to the blocking and independent quantization in the encoding process, visually annoying block effects are usually introduced into the compressed image, which seriously affects the subjective quality and utilization value of the image signal, especially at low bit rates. Image decompression is a classic but still attractive research area that reduces compression effects and improves image quality without changing the original coding standard. Reducing blocking artifacts produced during compression is an ill-posed image inverse problem. Different image priors are related to different structures and statistics of natural i...

Claims

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Application Information

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IPC IPC(8): H04N19/86H04N19/192H04N19/176H04N19/119G06T9/00
CPCG06T9/00H04N19/119H04N19/176H04N19/192H04N19/86
Inventor 何小海胡婧任超卿粼波熊淑华滕奇志王正勇
Owner SICHUAN UNIV
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