An image denoising method based on relu convolutional neural network

Abstract

The invention discloses an ReLU convolutional neutral network-based image denoising method. The method comprises the following steps of building an ReLU convolutional neutral network model, wherein the ReLU convolutional neutral network model comprises a plurality of convolutional layers and active layers after the convolutional layers, wherein the active layers are ReLU functions; selecting a training set, and setting training parameters of the ReLU convolutional neutral network model; training the ReLU convolutional neutral network model by taking a minimized loss function as a target according to the ReLU convolutional neutral network model and the training parameters of the ReLU convolutional neutral network model to form an image denoising neural network model; and inputting a to-be-processed image to the image denoising neural network model, and outputting a denoised image. According to the ReLU convolutional neutral network-based image denoising method disclosed by the invention, the learning ability of the neural network is greatly enhanced, accurate mapping from noisy images to clean images is established, and real-time denoising can be realized.

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 ReLU convolutional 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 satis...

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Problems solved by technology

However, this mixed model and low-rank estimation are not so accurate, so the denoising effect is not very good; and the time complexity and space complexity are high, which brings great inconvenience to practical applications

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