摘要
目前,深度卷积神经网络(Convolutional neural network,CNN)已主导了单图像超分辨率(Single image super-resolution,SISR)技术的研究,并取得了很大进展.但是,SISR仍是一个开放性问题,重建的超分辨率(Super-resolution,SR)图像往往会出现模糊、纹理细节丢失和失真等问题.提出一个新的逐像素对比损失,在一个局部区域中,使SR图像的像素尽可能靠近对应的原高分辨率(High-resolution,HR)图像的像素,并远离局部区域中的其他像素,可改进SR图像的保真度和视觉质量.提出一个组合对比损失的渐进残差特征融合网络(Progressive residual feature fusion network,PRFFN).主要贡献有:1)提出一个通用的基于对比学习的逐像素损失函数,能够改进SR图像的保真度和视觉质量;2)提出一个轻量的多尺度残差通道注意力块(Multi-scale residual channel attention block,MRCAB),可以更好地提取和利用多尺度特征信息;3)提出一个空间注意力融合块(Spatial attention fuse block,SAFB),可以更好地利用邻近空间特征的相关性.实验结果表明,PRFFN显著优于其他代表性方法.
Abstract
Deep convolutional neural network(CNN)has achieved great success in single image super-resolution(SISR).However,SISR is still an open issue,and reconstructed super-resolution(SR)images often suffer from blur-ring,loss of texture details and distortion.In this paper,a new pixel-wise contrastive loss is proposed to improve the fidelity and visual quality of SR images by making the pixels of SR images as close as possible to the corres-ponding pixels of the original high-resolution(HR)images and away from the other pixels in the local region.We also propose a progressive residual feature fusion network(PRFFN)with combined contrastive loss,and the main contributions include:1)A general pixel-wise loss function based on contrastive learning is proposed,which can im-prove the fidelity and visual quality of SR images;2)A lightweight multi-scale residual channel attention block(MRCAB)is proposed,which can better extract and utilize multi-scale feature information;3)A spatial attention fusion block(SAFB)is proposed,which can better utilize the correlation of neighboring spatial features.The experi-mental results demonstrate that PRFFN significantly outperforms other representative methods.
维普
万方数据