Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution

Abstract

Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due to large memory cost. To solve this problem, we propose a pyramidal dense attention network (PDAN) for lightweight image super-resolution in this paper. In our method, the proposed pyramidal dense learning can gradually increase the width of the densely connected layer inside a pyramidal dense block to extract deep features efficiently. Meanwhile, the adaptive group convolution that the number of groups grows linearly with dense convolutional layers is introduced to relieve the parameter explosion. Besides, we also present a novel joint attention to capture cross-dimension interaction between the spatial dimensions and channel dimension in an efficient way for providing rich discriminative feature representations. Extensive experimental results show that our method achieves superior performance in comparison with the state-of-the-art lightweight SR methods.

References in corpus (10)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Sketch-based Manga Retrieval using Manga109 Dataset
• CBAM: Convolutional Block Attention Module
• Image Super-Resolution Using Very Deep Residual Channel Attention Networks
• Residual Dense Network for Image Super-Resolution
• Learning Deep CNN Denoiser Prior for Image Restoration
• Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
• Interleaved Group Convolutions for Deep Neural Networks
• Fast and Accurate Single Image Super-Resolution via Information Distillation Network
• When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks