Single image super‐resolution based on progressive fusion of orientation‐aware features

• Published in: Pattern recognition Vol. 133; p. 109038
• Main Authors: He, Zewei, Chen, Du, Cao, Yanpeng, Yang, Jiangxin, Cao, Yanlong, Li, Xin, Tang, Siliang, Zhuang, Yueting, Lu, Zhe-ming
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2023
• Subjects: Channel attention; Feature extraction; Feature fusion; Orientation-aware; Single image super-resolution; 99-00; 00-01; Orientation-aware; Feature extraction; Feature fusion; Channel attention; Single image super-resolution
• DOI: 10.1016/j.patcog.2022.109038
• ISSN: 0031-3203

•We combined 1D and 2D convolutional kernels to extract orientation-aware features.•We employed a channel attention mechanism to adaptively select informative orientation-aware features.•Progressive feature fusion scheme is proposed to fuse hierarchical features. Single image super-resolution (SISR) is an active research topic in the fields of image processing, computer vision and pattern recognition, restoring high-frequency details and textures based on the low-resolution input image. In this paper, we aim to build more accurate and faster SISR models via developing better-performing feature extraction and fusion techniques. Firstly, we proposed a novel Orientation-Aware feature extraction/selection Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i.e., 3×1, 1×3, and 3×3) for extracting orientation-aware features. The channel attention mechanism is deployed within each OAM, performing scene-specific selection of informative outputs of the orientation-dependent kernels (e.g., horizontal, vertical, and diagonal). Secondly, we present an effective fusion architecture to progressively integrate multi-scale features extracted in different convolutional stages. Instead of directly combining low-level and high-level features, similar outputs of adjacent feature extraction modules are grouped and further compressed to generate a more concise representation of a specific convolutional stage for high-accuracy SISR task. Based on the above two important improvements, we present a compact but effective CNN-based model for high-quality SISR via Progressive Fusion of Orientation-Aware features (SISR-PF-OA). Extensive experimental results verify the superiority of the proposed SISR-PF-OA model, performing favorably against the state-of-the-art models in terms of both restoration accuracy and computational efficiency (e.g., SISR-PF-OA outperforms RCAN model, achieving higher PSNR 31.25 dB vs. 31.21 dB and using fewer FLOPs 764.41 G vs. 1020.28 G on the Manga109 dataset for scale factor ×4 SISR task.). The source codes will be made publicly available.