Image denoising method capable of quickly and effectively retaining edge and directional characteristics

Abstract

The invention discloses an image denoising method capable of quickly and effectively retaining edge and directional characteristics. After an image is subjected to wavelet transform, a low-frequency subimage centralizes most energy noise in an original image and is subjected to secondary Wiener filtering to highlight the details of a fusion image; since the image noise is mainly centralized in wavelet high-frequency subimage parts in three different directions, the coefficient of the image noise is small, denoising processing is carried out by singular value decomposition, a high-frequency diagonal subimage is subjected to the singular value decomposition with a high-frequency subimage in line direction or a high-frequency subimage in column direction after rotating to the line direction or the column direction, and meanwhile, the high-frequency subimage is subjected to edge extraction and retaining in order to avoid losing edge detail information; and finally, the denoised low-frequency and high-frequency subimages are subjected to inverse wavelet transform to reconstruct a final denoising image. Singular value numbers required for singular value reconstruction images are jointly determined through a peak signal to noise ratio of the image and a traditional method.

Description

technical field [0001] The invention belongs to the field of signal and information processing, in particular to an image denoising method suitable for noise-containing images appearing in the process of image generation or transmission. Background technique [0002] Due to the interference and influence of various noises in the process of image generation or transmission, there will inevitably be item degradation, there are different degrees of blurred edges, poor local and overall contrast, etc., which will affect the quality of subsequent images. Processing (such as segmentation, compression, and image understanding, etc.) can have adverse effects. Therefore, denoising the image to improve the image quality is a basic and important work in image processing. [0003] Wavelet transform adopts a multi-resolution method, which has low entropy, decorrelation and base selection flexibility, while the noise information of the image is mainly concentrated in the high-frequency p...

AI Technical Summary

Problems solved by technology

However, the general simple singular value filtering method does not take into account the directional characteristics of singular value filtering, and the noise of the image is only distributed in the high frequency part of the wavelet transform frequency domain, and because these high frequency parts have horizontal, vertical, diagonal ( 45° / 135°) directional characteristics, so it can be considered to perform singular value decomposition on the high-frequency parts of the three directions after wavelet transformation to achieve the purpose of filtering and denoising

[0007] In addition, since the number of singular values ​​required to reconstruct an image in the traditional singular value decomposition denoising process depends on traditional empirical values, or is determined by comparing the image quality of reconstructed images with different numbers of singular values ​​through multiple calculations, so It affects the denoising effect of the image, and also seriously affects the timeliness of the algorithm

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