基于空间特征变换与反投影的渐进式图像超分辨

Image super-resolution based on spatial feature transformation and back projection progressive reconstruction network

基于深度网络的单帧图像超分辨(SISR)方法为目前SR研究热点,但是多数该类方法在特征提取时主要侧重在网络深度结构的探索,忽略了中间空间特征层之间的相似性,并且在重构时忽略了特征层之间的特征差异性。针对上述问题,提出了基于空间特征变换与反投影重构的渐进式网络。该方法的主要特征是,在图像特征提取时对特征空间进行特征仿射变换,从而获得渐进式特征和空间变换特征,增加特征层间的不同相似性。在图像重构阶段,重构模块采用多尺度反投影的策略融合了图像多源特征,从而使得其模块更加注重特征之间的差异性。实验结果表明,相比大多数超分辨算法,所提方法在图像超分辨重建时PSNR/SSIM等评估指标均有较大提升,且重构图像的纹理信息也更加丰富。

The single-frame image super-resolution(SISR)method based on deep networks is currently a hotspot in SR research.However,most of these methods mainly focus on exploring the network depth structure during feature extraction,ignoring the similarity between the feature layers in the intermediate space,and neglect the feature differences between the feature layers during reconstruction.This paper proposed a progressive network based on spatial feature transformation and back-projection reconstruction to solve this problem.The main feature of this method was to perform feature affine transformation on the feature space when extracting image features to increase the different similarities between feature layers,so as to obtain progressive features and spatial transformation features.In the image reconstruction stage,the reconstruction module adopted the strategy of multi-scale back projection to integrate the multi-source features of the image,which made the module pay more attention to the difference between features.Finally,the experimental results show that compared with most super-resolution methods,the proposed method has greatly improved the evaluation indexes such as PSNR/SSIM during image super-resolution reconstruction,and the texture information of the reconstructed image is more abundant.