Contourlet domain Wiener filtering image denoising method based on two-dimensional Otsu

Abstract

The invention discloses a contourlet domain Wiener filtering image denoising method based on two-dimensional Otsu, which mainly solves the problem that a denoising effect of a traditional denoising method is not good. The contourlet domain Wiener filtering image denoising method based on the two-dimensional Otsu comprises the following implementation steps of: 1, performing contourlet decomposition on a noise-contained image TI; 2, respectively performing two-dimensional Otsu partition on all decomposed high-frequency sub-bands to obtain an important coefficient and a non-important coefficient; 3, respectively calculating elliptical windows of the high-frequency sub-bands, estimating signal variances of the high-frequency sub-bands according to the elliptical windows, and respectively performing Wiener filtering on the important coefficient and the non-important coefficient; 4, performing contourlet inverse transformation on the denoised high-frequency sub-bands to obtain a denoised image FI; and 5, performing non-local average filtering on the denoised image FI to obtain denoised output. According to the contourlet domain Wiener filtering image denoising method based on the two-dimensional Otsu, noise in a natural image containing Gaussian white noise can be effectively removed. The contourlet domain Wiener filtering image denoising method based on the two-dimensional Otsu can be used for preprocessing the image when change detection and target identification are performed.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to the denoising of natural images under the condition of noise, and can be used for digital image preprocessing in the fields of forest resource investigation, environmental change assessment, medical imaging and the like. Background technique [0002] Due to the limitations of various conditions, images will be affected by various noises in the process of acquisition, encoding, and transmission. Image denoising solves the problem of image quality degradation caused by noise interference, suppresses the influence of noise, and improves image quality. Therefore, image denoising technology occupies an important position in many fields. [0003] The most widely used image denoising methods are filtering methods based on wavelet transform and contourlet transform. The contourlet transform uses an adjustable rectangular basis function, which can accurately capture the edges in the im...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the image signal is lost too much in the first step of non-local mean pre-filtering, resulting in the estimated Wiener shrinkage factor being too small, and the denoised image still contains a lot of noise

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