Single Image Super-Resolution by Residual Recovery Based on an Independent Deep Convolutional Network

IEEE Access

Mali Gong¹  Fei Wang¹ 

[1] Department of Precision Instruments, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China;

关键词: Single image super-resolution;    independent deep convolutional neural netowork (IDCNN);    image residual recovery;    ToF images;   

DOI  :  10.1109/ACCESS.2020.2986365

来源: DOAJ

【 摘 要 】

In this paper, we propose an independent neural network for single image super-resolution by residual recovery. The network is inspired by the observation that there still exists image residuals between the low-resolution image and the downsampled high-resolution output obtained by a previously proposed super-resolution network. Based on this observation, we design a simple but effective deep convolutional neural network to train the mapping between the image residuals and the corresponding ground-truth residuals. Furthermore, we combine the high-resolution output generated by the previous super-resolution network and the high-resolution residual output by the proposed neural network to yield the final high-resolution image. Extensive experiments on simulated natural images and real time-of-flight (ToF) images demonstrate the effectiveness of the proposed method from the aspects of visual and quantitative performance.

【 授权许可】

Unknown