Improved adaptive threshold wavelet denoising algorithm based on LoG operator

Abstract

The invention discloses an improved threshold denoising algorithm and provides an improved adaptive threshold denoising algorithm based on a LoG operator. The improved adaptive threshold denoising algorithm based on the LoG operator aims to solve the problem that in the denoising process, the denoising effect of the edge part and the non-edge part of an image is poor. The improved adaptive threshold denoising algorithm based on the LoG operator comprises the steps that firstly, edge contour information of the image is extracted through the LoG operator; secondly, the non-edge part of the image is denoised, a threshold correction coefficient is added to a soft threshold function, and then a new threshold function is established; thirdly, the edge part of the image is denoised, energy nearby a wavelet coefficient and a threshold are combined, and then a new threshold function is established; fourthly, an R channel, a G channel and a B channel of the image are processed, and all detail information of the image is reserved. Experimental results show that by means of the improved adaptive threshold denoising algorithm based on the LoG operator, edge information of the image is effectively stored, and the comprehensive denoising effect is improved remarkably.

Description

technical field [0001] The invention relates to a denoising algorithm, in particular to an improved adaptive threshold wavelet denoising algorithm based on LoG operator. Background technique [0002] With the advancement of science and technology, and the increasing popularity of various digital products and electronic devices, people are used to obtaining information about things in the form of images or videos, but the image transmission process will be interfered by various noises, resulting in image quality degradation. Therefore, in the process of image transmission, noise reduction processing should be performed on it. [0003] At present, the commonly used wavelet denoising algorithms include hard threshold algorithm, soft threshold algorithm, traditional wavelet threshold denoising and edge-based threshold algorithm proposed by various literatures. However, the effect of these algorithms is not satisfactory when processing images. For example, the hard threshold alg...

AI Technical Summary

Problems solved by technology

[0003] At present, the commonly used wavelet denoising algorithms include hard threshold algorithm, soft threshold algorithm, traditional wavelet threshold denoising and edge-based threshold algorithm proposed by various literatures. However, the effect of these algorithms is not satisfactory when processing images.

For example, the hard threshold algorithm, in the denoising process, although it can retain the image detail information, but most of the noise is also preserved; the image processed by the soft threshold algorithm, the edge information of the image is too blurred, and the outline is not clear enough; the traditional wavelet threshold Denoising algorithm, because the same threshold is used for the entire image, this leads to a large loss of detail information in the edge part, resulting in blurred images; the edge-based threshold algorithm proposed by various documents is improved on the basis of the traditional wavelet threshold algorithm , but because the threshold function is not properly selected, the denoising effect is still poor

The theory of the human visual system shows that the human eye is more sensitive to details such as edges, so the loss of edge information will lead to a decrease in the visual quality of the image

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