An image denoising method based on multi-scale parallel CNNs

Abstract

The invention discloses an image denoising method based on multi-scale parallel CNNs, comprising five steps: 1, building a multi-scale parallel convolution neural network model, wherein only that convolution layer and the activation lay are included, and residual learning is added at the same time; 2, setting training parameter of a multi-scale parallel convolution neural network model; 3, selecting a training set and cutting and flipping the selected training image to enhance the number of the training sets; 4, selecting the mean square error as a loss function and train a multi-scale parallel convolution neural network model with a minimization loss function to obtain an image denoising model; 5, inputting the noise image of arbitrary size to the image denoising model, and outputting thedenoised clean image. The invention can preserve the edge information and the detail information of the image as much as possible while denoising, can improve the structural similarity of the image,and can obtain a high-quality denoised image.

Description

technical field [0001] The invention relates to the fields of computer vision and digital image processing, in particular to an image denoising method based on multi-scale parallel CNN. Background technique [0002] Due to the inevitable influence of the surrounding environment, equipment, human factors, etc. in the process of image acquisition, the obtained image is always noisy, and the noise will deteriorate the quality of the image, thus affecting the readability of the image and the image subsequent processing. The task of image denoising is to remove the noise from the image to reduce the influence of noise on the image. At present, there are many classic methods for image denoising, but they can be roughly divided into two categories, one is based on spatial domain filtering, such as mean filtering, median filtering, etc.; the other is based on transform domain filtering, such as Gaussian scale mixture model Bayesian least squares in . These local filtering methods...

AI Technical Summary

Problems solved by technology

These local filtering methods neither filter in the global scope nor take into account the connection between natural image blocks and blocks, so the denoising effect obtained is not satisfactory

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