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.