Image restoration method based on enhanced neural network, storage medium and system

Abstract

The invention discloses an image restoration method based on an enhanced neural network. The method comprises the following steps: S1, converting an image to be restored into a plurality of low-resolution images under different zoom factors; S2, converting the image to be restored into a plurality of low-resolution images under different zoom factors; S3, converting the image to be restored into aplurality of low-resolution images. S2, respectively inputting a plurality of low-resolution images to a first depth convolution neural network trained in advance, thereby obtaining a plurality of high-resolution images under corresponding different scaling factors; S3, converting a plurality of high-resolution images in S2 into images having the same size as the image to be restored, and fusingthese images to obtain the restored images. The invention also discloses a corresponding storage medium and an image restoration system. The invention can prevent the network from being degraded in the training process and accelerate the convergence speed.

Description

technical field [0001] The invention relates to the field of image restoration, in particular to an image restoration method, storage medium and system based on an enhanced neural network. Background technique [0002] With the development of network technology and communication technology, image processing is applied in more and more fields, such as: aviation exploration, weather prediction, disaster rescue and video entertainment, etc. However, the shooting equipment will be affected when taking pictures in smog, rain, snow, dark light and equipment shaking. This kind of problem of obtaining the original image through restoration is called the image restoration problem. The image restoration problem is a classic computer vision and model recognition problem. Among them, image super-resolution and image denoising are typical and important image restoration problems, which aim to restore high-resolution images from low-resolution images, which have been applied to medical ...

AI Technical Summary

Problems solved by technology

However, although the above methods have achieved certain effects on the image restoration problem, as the network gets deeper, the network degradation phenomenon becomes more and more serious, and the convergence speed is slow during the network training process.

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