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Image restoration tasks have witnessed great performance improvement inrecent years by developing large deep models. Despite the outstandingperformance, the heavy computation demanded by the deep models has restrictedthe application of image restoration. To lift the restriction, it is requiredto reduce the size of the networks while maintaining accuracy. Recently, N:Mstructured pruning has appeared as one of the effective and practical pruningapproaches for making the model efficient with the accuracy constraint.However, it fails to account for different computational complexities andperformance requirements for different layers of an image restoration network.To further optimize the trade-off between the efficiency and the restorationaccuracy, we propose a novel pruning method that determines the pruning ratiofor N:M structured sparsity at each layer. Extensive experimental results onsuper-resolution and deblurring tasks demonstrate the efficacy of our methodwhich outperforms previous pruning methods significantly. PyTorchimplementation for the proposed methods will be publicly available athttps://github.com/JungHunOh/SLS_CVPR2022.

Attentive Fine-Grained Structured Sparsity for Image Restoration