Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution

• URL: http://arxiv.org/abs/2302.13800v1
• Date: Mon, 27 Feb 2023 14:19:31 GMT
• Title: Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution
• Authors: Long Sun, Jiangxin Dong, Jinhui Tang, Jinshan Pan
• Abstract summary: We develop a spatially-adaptive feature modulation (SAFM) mechanism upon a vision transformer (ViT)-like block. Proposed method is $3times$ smaller than state-of-the-art efficient SR methods.
• Score: 90.16462805389943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although numerous solutions have been proposed for image super-resolution, they are usually incompatible with low-power devices with many computational and memory constraints. In this paper, we address this problem by proposing a simple yet effective deep network to solve image super-resolution efficiently. In detail, we develop a spatially-adaptive feature modulation (SAFM) mechanism upon a vision transformer (ViT)-like block. Within it, we first apply the SAFM block over input features to dynamically select representative feature representations. As the SAFM block processes the input features from a long-range perspective, we further introduce a convolutional channel mixer (CCM) to simultaneously extract local contextual information and perform channel mixing. Extensive experimental results show that the proposed method is $3\times$ smaller than state-of-the-art efficient SR methods, e.g., IMDN, in terms of the network parameters and requires less computational cost while achieving comparable performance. The code is available at https://github.com/sunny2109/SAFMN.

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