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Image denoising method based on self-adaptive wavelet threshold and two-sided filter

A bilateral filter and adaptive threshold technology, applied in the field of image processing, can solve problems such as image edge blur

Inactive Publication Date: 2014-04-02
BEIJING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The shortcomings of the existing methods: on the one hand, the traditional wavelet threshold denoising method does not consider the factor that the selected threshold should be adaptively changed with the change of the wavelet decomposition scale and sub-band; on the other hand, the wavelet threshold denoising method While removing noise, high-frequency details may also be removed, blurring the edges of the image, and prone to noticeable artifacts such as low-frequency noise and edge ringing

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Embodiment Construction

[0028] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0029] Such as figure 1 As shown, the denoising algorithm based on adaptive wavelet threshold and bilateral filter of the embodiment of the present invention comprises the following steps:

[0030] Step S1, using discrete wavelet to decompose the image to obtain multiple subbands and their wavelet coefficients.

[0031] In one embodiment of the present invention, firstly, an image signal containing noise is read in, that is, the image g containing noise. Among them, the pixel value of the noisy image g at the pixel point (i,j) can be e...

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Abstract

The invention provides an image denoising method based on a self-adaptive wavelet threshold and a two-sided filter and aims to improve the effect of a wavelet threshold denoising algorithm and better protect the edge and the detailed information of an image. The algorithm comprises the following steps of decomposing the image by adopting a discrete wavelet to obtain a plurality of sub-bands and the wavelet coefficients of the sub-bands; selecting a threshold which is self-adaptively changed along with the changes of wavelet-decomposing scales and the sub-bands, and carrying out quantitative threshold processing by adopting a soft threshold function; carrying out inverse wavelet transformation to obtain a reconstructed image; filtering the reconstructed image by adopting the two-sided filter so as to obtain the clear image. According to the image denoising method, wavelet threshold denoising is carried out by utilizing the threshold which is self-adapted to the wavelet-decomposing scales and the sub-bands, and filtering is carried out by combining the two-sided filter, so that through the designed denoising algorithm, not only can white gaussian noise be effectively removed, but also the edge and the detailed information of the image can be well reserved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a denoising method based on an adaptive wavelet threshold and a bilateral filter. Background technique [0002] In recent years, wavelet transform has been widely used in the field of image denoising because of its characteristics of multi-resolution analysis, the ability to characterize signals well in the time-frequency domain, and the window with fixed size and variable shape. The commonly used wavelet domain denoising method is to shrink the wavelet coefficients, that is, the wavelet threshold denoising method. Among them, the classic threshold is the unified threshold proposed by Donoho and Johnstone that depends on the noise standard deviation and signal length. [0003] The shortcomings of the existing methods: on the one hand, the traditional wavelet threshold denoising method does not consider the factor that the selected threshold should be adaptively changed ...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 刘芳邓志仁付凤之马玉磊
Owner BEIJING UNIV OF TECH
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