Dual Aggregation Transformer for Image Super-Resolution

• URL: http://arxiv.org/abs/2308.03364v2
• Date: Fri, 11 Aug 2023 05:21:15 GMT
• Title: Dual Aggregation Transformer for Image Super-Resolution
• Authors: Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, Fisher Yu
• Abstract summary: We propose a novel Transformer model, Dual Aggregation Transformer, for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner. Our experiments show that our DAT surpasses current methods.
• Score: 92.41781921611646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer has recently gained considerable popularity in low-level vision tasks, including image super-resolution (SR). These networks utilize self-attention along different dimensions, spatial or channel, and achieve impressive performance. This inspires us to combine the two dimensions in Transformer for a more powerful representation capability. Based on the above idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT), for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner. Specifically, we alternately apply spatial and channel self-attention in consecutive Transformer blocks. The alternate strategy enables DAT to capture the global context and realize inter-block feature aggregation. Furthermore, we propose the adaptive interaction module (AIM) and the spatial-gate feed-forward network (SGFN) to achieve intra-block feature aggregation. AIM complements two self-attention mechanisms from corresponding dimensions. Meanwhile, SGFN introduces additional non-linear spatial information in the feed-forward network. Extensive experiments show that our DAT surpasses current methods. Code and models are obtainable at https://github.com/zhengchen1999/DAT.

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