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ShuffleMixer: An Efficient ConvNet for Image Super-Resolution
[article]
2022
arXiv
pre-print
Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation. In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design. Specifically,
arXiv:2205.15175v1
fatcat:2oyhlmdw4zdjxjn2knbgokea6q