Lightweight Image Super-Resolution Reconstruction With Hierarchical Feature-Driven Network
Li Wen, Sumei Li, Anqi Liu · 2020
Deep convolutional neural networks (CNNs) have emerged as powerful tool for single image super-resolution (SISR). However, enormous parameters hinder their real-world applications. To address this issue, we propose a lightweight hierarchical feature-driven network (HFDN) that can fully explore local and global hierarchical feature information. Specifically, we devise a hierarchical fuse module (HFM), which contains an adaptive dense unit (ADU) and an enhancement unit (EU), to effectively utilize local hierarchical information and encourage layer-wise feature reuse. Further, we introduce the channel and spatial attention mechanisms to emphasize informative details. In addition, we propose the multi-supervised reconstruction (MSR) strategy, which amplifies feature maps at different levels of network to exploit global hierarchical information and recover high-quality image. Experimental results on the benchmark datasets prove that the proposed network performs superior to the state-of-the-art methods both quantitatively and qualitatively.