DCS-RISR: Dynamic channel splitting for efficient real-world image super-resolution

doi:10.1016/j.neunet.2024.107119

DCS-RISR: Dynamic channel splitting for efficient real-world image super-resolution

2025-04-01

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DOI：10.1016/j.neunet.2024.107119

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Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low- resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR. Specifically, we first introduce the light degradation prediction network to regress the degradation vector to simulate the real-world degradations, upon which the channel splitting vector is generated as the input for an efficient SR model. Then, a learnable octave convolution block is proposed to adaptively decide the channel splitting scale for low- and high-frequency features at each block, reducing computation overhead and memory cost by offering the large scale to low-frequency features and the small scale to the high ones. To further improve the RISR performance, non- local regularization is employed to supplement the knowledge of patches from LR and HR subspace with free-computation inference. Extensive experiments demonstrate the effectiveness of DCS-RISR on different benchmark datasets. Our DCS-RISR not only achieves the best trade-off between computation/parameter and PSNR/SSIM metric, but also effectively handles real-world images with different degradation levels.

Keywords:

Dynamic channel splitting

Efficient super-resolution

Real-world image

Frequency feature

Non-local regularization

DCS-RISR: Dynamic channel splitting for efficient real-world image super-resolution

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