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De-noising method of filtering images in size adaptive block matching transform domains

A filtering image and block matching technology, applied in the field of image denoising, can solve problems such as limiting the performance of BM3D algorithms

Inactive Publication Date: 2012-09-19
TAISHAN UNIV
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AI Technical Summary

Problems solved by technology

The size of all blocks in the block matching process in the BM3D algorithm is a fixed value, which also limits the performance of the BM3D algorithm

Method used

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  • De-noising method of filtering images in size adaptive block matching transform domains
  • De-noising method of filtering images in size adaptive block matching transform domains
  • De-noising method of filtering images in size adaptive block matching transform domains

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Experimental program
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Effect test

Embodiment Construction

[0058] Comparison of experimental results

[0059] Average Structural Similarity Measure

[0060] Before comparing the experimental results, a new objective evaluation method for image quality is introduced. The Mean Structural Similarity Measure (MSSIM) is the 2004 Z.Wang et al. [135] A more effective image quality evaluation method than PSNR is proposed, which is an objective evaluation method of image results widely used in the current image denoising research field. The specific method for evaluating image denoising results with MSSIM is given below:

[0061] First divide the real image and the denoised image into M blocks respectively, and calculate the mean and standard deviation of each real image block x and each denoised image block y respectively:

[0062] μ x = 1 N Σ i = 1 N x...

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Abstract

The invention discloses a de-noising method of filtering images in size adaptive block matching transform domains. Less false signals are introduced as two-dimension transformation of each image block in the block matching 3D (BM3D) in a basic estimation stage is abandoned by the method; image details can be well preserved as the block number in blocking matching groups of the method is less than the block number in the BM3D method. The image de-noising performance of the method is further improved as the method adaptively selects the block size based on form components during block matching. The current general objective evaluation of image de-nosing includes peak signal noise ratio (PSNR) and mean structural similarity (MSSIM), and according to the method, the de-noising calculation results of a plurality of standard images provided on BM3D networks are higher than the results of the BM3D method on the basis of the two objective evaluations and under all noise intensities.

Description

technical field [0001] The invention belongs to the field of computer image processing, in particular to an image denoising method. Background technique [0002] In the process of image acquisition, it is inevitable to introduce various noises, and the study of image denoising has been a hot research topic in the past few decades. Currently the best image denoising method is Block Matching 3D Transform Domain Collaborative Filtering (BM3D) [Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3D transform domain collaborative filtering. IEEE Transactions on Image Processing, 2007, 16 (8): 2080-2095], because this method effectively combines the local transformation method and the non-local filtering method, it is recognized as the best image denoising method at present. This method divides the whole process of image denoising into two stages: the first stage is block-matching three-dimensional transform domain hard-threshold coefficient shrinkage, called basic esti...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 侯迎坤杨德运
Owner TAISHAN UNIV
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