MRFN: Multi-Receptive-Field Network for Fast and Accurate Single Image Super-Resolution

• Published in: IEEE transactions on multimedia Vol. 22; no. 4; pp. 1042 - 1054
• Main Authors: He, Zewei, Cao, Yanpeng, Du, Lei, Xu, Baobei, Yang, Jiangxin, Cao, Yanlong, Tang, Siliang, Zhuang, Yueting
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Back propagation; Computational modeling; Convolutional neural networks; deep learning; Feature extraction; Image reconstruction; Image resolution; Image restoration; loss function; Modules; multi-receptive-field; Restoration; Single image super-resolution; Training

Recently, convolutional neural network (CNN) based models have shown great potential in the task of single image super-resolution (SISR). However, many state-of-the-art SISR solutions are reproducing some tricks proven effective in other vision tasks, such as pursuing a deeper model. In this paper,...