2022
DOI: 10.1109/access.2022.3211302
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Arbitrary Scale Super-Resolution Neural Network Based on Residual Channel-Spatial Attention

Abstract: In recent years, the performance of convolutional neural networks in single-image superresolution has improved significantly. However, most state-of-the-art models address the super-resolution problem for specific scale factors. In this paper, we propose a convolutional neural network for arbitrary scale super-resolution. Specifically, given a range of scale factors, the proposed model can generate superresolution images with non-integer scale factors within the range. The proposed model incorporates a channel… Show more

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