A composite degraded image high-quality reconstruction method based on a generative adversarial network

Abstract

The invention discloses a composite degraded image high-quality reconstruction method based on a generative adversarial network. The method is mainly used for low-quality images with various quality reduction problems including haze, system noise, low illumination and compression distortion and the like. According to the method, a composite degraded image high-quality reconstruction method based on a generative adversarial network is established from the perspective of composite factor degraded image reconstruction, and reconstruction of a degraded image combined by factors such as haze, low illumination, compression, system noise and optical blurring can be completed; Secondly, an asymmetric generation network is adopted, so that the parameter quantity of the model is greatly reduced, andthe model is easy to train and use; Furthermore, the end-to-end idea is adopted, so that the architecture of the reconstruction system is simplified, and preprocessing and post-processing are omitted; And finally, the generation network is completely composed of convolution layers, and a composite degraded image with any size can be input for reconstruction.

Description

technical field [0001] The invention belongs to the field of digital image / video signal processing, in particular to a method for high-quality reconstruction of composite degraded images based on generating confrontation networks. Background technique [0002] In video surveillance, intelligent transportation, military imaging reconnaissance, missile precision imaging guidance, remote sensing survey, aerial surveying and mapping and other applications, outdoor vision systems are vulnerable to haze, system noise, low illumination, optical blur, compression and other factors. The random combination of these factors in a complicated way will lead to serious degradation of image quality, such as image detail loss, contrast drop, color distortion, compression block effect and other phenomena, and the subjective visual effect of the image will become very poor. At the same time, the degradation of image quality can seriously affect the effectiveness of outdoor vision systems. Wha...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art, and propose a composite degraded image based on a generative adversarial network architecture for low-quality images containing haze, system noise, low illumination and compression distortion. Quality reconstruction method, so that it can simultaneously solve the problem of low-quality image reconstruction caused by problems such as haze, system noise, low illumination and compression distortion

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