Convolutional neural network (CNN)-based methods have achieved great success for single-image superresolution (SISR). However, most models attempt to improve reconstruction accuracy while increasing the requirement of number of model parameters. To tackle this problem, in this paper, we study reducing the number of parameters and computational cost of CNN-based SISR methods while maintaining the accuracy of super-resolution reconstruction performance. To this end, we introduce a novel network architecture for SISR, which strikes a good trade-off between reconstruction quality and low computational complexity. Specifically, we propose an iterative back-projection architecture using sub-pixel convolution instead of deconvolution layers. We evaluate the performance of computational and reconstruction accuracy for our proposed model with extensive quantitative and qualitative evaluations. Experimental results reveal that our proposed method uses fewer parameters and reduces the computational cost while maintaining reconstruction accuracy against state-of-the-art SISR methods over well-known four SR benchmark datasets. Code is available at "https://github.com/supratikbanerjee/SubPixel-BackProjection_SuperResolution".
Efficient Image Super-Resolution Using Pixel Attention
Chinese Academy of Sciences（中国科学院）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人社会应用研究院SIAT分院）；SIAT-SenseTime Joint Lab（SIAT-商汤科技联合实验室）；ShenZhen Key Lab of Computer Vision and Pattern Recognition（深圳市计算机视觉与模式识别重点实验室）
Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration
National University of Defense Technology（国防科技大学）；HKUST(GZ)（香港理工大学）；Qilu University of Technology（青岛科技大学）；Technical University of Munich（慕尼黑技术大学）；TeleAI, China Telecom（TeleAI，中国电信）；Beihang University（北航）
A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learning
China University of Geosciences, Wuhan（中国地质大学（武汉））；Henan Key Laboratory of Network Cryptography Technology（河南省网络密码技术重点实验室）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Henan University of Chinese Medicine（河南中医药大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
