An image denoising method based on cascaded residual neural network

Abstract

The invention discloses an image denoising method based on a cascaded residual neural network, comprising the following steps: building a cascaded residual neural network model, the cascaded residual neural network model is formed by connecting a plurality of residual units in series , wherein each of the residual units includes a plurality of convolutional layers, activation layers and unit skip connection units after each of the convolutional layers; select a training set, and set the training of the cascaded residual neural network model Parameters; according to the cascaded residual neural network model and its training parameters, the cascaded residual neural network model is trained with the goal of minimizing the loss function to form an image denoising neural network model; the image to be processed is input to the The image denoising neural network model is described, and the image after denoising is output. The image denoising method based on the cascaded residual neural network disclosed by the invention greatly enhances the learning ability of the neural network, establishes accurate mapping from noise images to clean images, and can realize real-time denoising.

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 a cascaded residual neural network. Background technique [0002] Image denoising is a classic and fundamental problem in computer vision and image processing. It is a necessary preprocessing process to solve many related problems. Its purpose is to restore a potential clean image x from a noisy image y. The process can be expressed as: y=x+n, where n is usually considered as Additive White Gaussian (AWG), which is a typical ill-conditioned linear inverse problem. In order to solve this problem, many early methods are solved by local filtering, such as Gaussian filtering, median filtering, bilateral filtering, etc. These local filtering methods neither filter in the global scope nor consider the relationship between natural image blocks and The connection between blocks, so the obtained denoising effect is not satisf...

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Problems solved by technology

However, these methods do not make full use of the NSS image blocks of noisy images and clean images at the same time, resulting in inaccurate models; in addition, the denoising process of these methods takes a lot of time, and is not robust to image noise and resolution. characteristics, lack of practical application value

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