Abstract
Recently, Transformer-based methods have made significant progress on image super-resolution. They encode long-range dependencies between image patches through self-attention mechanism. However, when extracting all tokens from the entire feature map, the computational cost is expensive. In this paper, we propose a novel lightweight image super-resolution approach, pixel integration network(PIN). Specifically, our method employs fine pixel integration and coarse pixel integration from local and global receptive field. In particular, coarse pixel integration is implemented by a retractable attention, consisting of dense and sparse self-attention. In order to focus on enriching features with contextual information, spatial-gate mechanism and depth-wise convolution are introduced to multi-layer perception. Besides, a spatial frequency fusion block is adopted to obtain more comprehensive, detailed, and stable information at the end of deep feature extraction. Extensive experiments demonstrate that PIN achieves the state-of-the-art performance with small parameters on lightweight super-resolution.
Published Version
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