Frequency Separation Generative Adversarial Super-resolution Reconstruction Network Based on Dense Residual and Quality Assessment
With generative adversarial networks have attracted much attention because they provide new ideas for blind super-resolution reconstruction.Considering the problem that the existing methods do not fully consider the low-frequency retention characteristics during image degradation,but use the same processing method for high and low-frequency components,which lacks the effective use of frequency details and is difficult to obtain better reconstruction result,a frequency separation generative adversarial super-resolution reconstruction network based on dense residual and quality assessment is proposed.The idea of frequency separation is adopted by the network to process the high-frequency and low-frequency information of the image separately,so as to improve the ability of capturing high-frequency information and simplify the processing of low-frequency features.The base block in the generator is designed to integrate the spatial feature transformation layer into the dense wide activation residuals,which enhances the ability of deep feature representation while differentiating the local information.In addition,no-reference quality assessment network is designed specifically for super-resolution reconstructed images using Visual Geometry Group(VGG),which provides a new quality assessment loss for the reconstruction network and further improves the visual effect of reconstructed images.The experimental results show that the method has better reconstruction effect on multiple datasets than the current state-of-the-art similar methods.It is thus shown that super-resolution reconstruction using generative adversarial networks with the idea of frequency separation can effectively utilize the image frequency components and improve the reconstruction effect.
万方数据
维普
