Improved wavelet transform and convolutional neural network image denoising method

Abstract

The invention discloses an improved wavelet transform and convolutional neural network image denoising method. The method comprises the following steps of 1, performing size normalization, noise addition and data expansion on an original digital image; 2, constructing an image denoising network model WTCNN, constructing a wavelet coefficient model in order to obtain features of a noise-containing image, transforming the input noise-containing image into wavelet coefficients of different sub-bands by adopting stationary wavelet transform SWT, obtaining input data of a convolutional neural network, and then constructing a model structure combining two image denoising algorithms; and 3, performing simulation denoising on a noise-containing image in a real scene through the ideal denoising model obtained in the step 2, and obtaining a denoised image. According to the invention, the network performance of the denoising algorithm is improved.

Description

technical field [0001] The invention belongs to the field of digital image processing, in particular to an image denoising method. Background technique [0002] Digital image has become one of the important data carriers in today's society due to its excellent characteristics of carrying a large amount of information and being easy to transmit. At the same time, with the rapid development of digital image technology, people's requirements for image quality are increasing. More and more Researchers and engineers are working hard on how to generate finer and better-quality images to meet people's demand for high-quality images. Image denoising (Image denoising) is to use various technical means to filter or suppress the noise in the image to improve the quality of the image, so that the image can express richer information more accurately, and provide information for subsequent image segmentation, target recognition, etc. The image processing link provides the basis. As many...

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Problems solved by technology

[0006] In order to overcome the singleness of traditional image denoising methods in terms of noise removal and the reduction of available image information in high-noise environments, the present invention provides an image denoising method based on wavelet transform and convolutional neural network. Allows two denoising algorithms to be combined, improving the network performance of the denoising algorithm

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