Thresholding image denoising method based on nonsubsampled Contourlet transformation

A non-subsampling, image technology, applied in the field of thresholding image denoising based on non-subsampling Contourlet transform, which can solve problems affecting image quality, interference, and Contourlet transform without translation invariance

Inactive Publication Date: 2015-06-24
HOHAI UNIV
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Problems solved by technology

However, because the Contourlet transform is not translation invariant, the processed image is disturbed by the pseudo-Gibbs phenomenon, which seriously affects the quality of the processed image.

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  • Thresholding image denoising method based on nonsubsampled Contourlet transformation
  • Thresholding image denoising method based on nonsubsampled Contourlet transformation
  • Thresholding image denoising method based on nonsubsampled Contourlet transformation

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[0045] Below in conjunction with accompanying drawing, technical scheme is described in detail:

[0046] Such as figure 1As shown, the thresholded image denoising method based on non-subsampling Contourlet transform provided by the present invention specifically includes the following steps:

[0047] Step 1: Select the test object, add Gaussian noise, and get the noise image;

[0048] Step 2: Build a model for NSCT of noisy images: in, Respectively represent the noisy image, the original image and the noise after NSCT, when the scale is k, the coefficient in the j-th direction, k=0,1,...,K-1; j=0,1,...J-1, K is NSCT The total number of scales for decomposition, J is the number of directions for decomposition at the kth layer. Perform a three-layer non-subsampling Contourlet transform on the noisy image. According to the order of the scale from coarse to fine, the number of direction subbands is 8, 8, and 16, and the subband coefficients on the kth scale and jth direction...

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Abstract

The invention discloses a thresholding image denoising method based on nonsubsampled Contourlet transformation, and the problem that the effect of an existing denoising method is bad is mainly solved. The realization method comprises the steps of inputting a noisy image, conducting the nonsubsampled Contourlet transformation on the noisy image, conducting a sub-band noise variance estimation and an estimation of a standard error of a coefficient in a transform domain, setting a reasonable threshold, conducting threshold processing on an NSCT coefficient through an improved semi-soft threshold function without processing a low frequent component, and obtaining a de-noised image through conducting nonsubsampled Contourlet inverse transformation on a processed NSCT coefficient. According to the thresholding image denoising method based on the nonsubsampled Contourlet transformation, noise in a natural image with white Gaussian noise can be effectively removed, and image information edges are kept as much as possible at the same time, so that the best recovery of an original image is obtained, and the thresholding image denoising method based on the nonsubsampled Contourlet transformation has good application prospect and great development potential.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to a thresholding image denoising method based on non-subsampling Contourlet transform. Background technique [0002] Image denoising is a very important research field in image processing. One of the problems is to remove the noise while trying not to destroy the information of the original image. Therefore, the ideal image denoising needs to achieve two goals: the first maximum Suppress noise; the second is to preserve the detailed feature information of the image as much as possible. The traditional noise reduction method largely removes the noise of the image, and also removes the useful high-frequency information of the image part. With the development of wavelet transform, image denoising using wavelet transform has become an active research topic. The multi-scale geometric analysis developed in recent years has also become a favorable tool for image denoisin...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 汪飞陈亮曹宁鹿浩毛明禾胡一帆
Owner HOHAI UNIV
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