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Inspired by the robustness and efficiency of sparse representation in sparsecoding based image restoration models, we investigate the sparsity of neuronsin deep networks. Our method structurally enforces sparsity constraints uponhidden neurons. The sparsity constraints are favorable for gradient-basedlearning algorithms and attachable to convolution layers in various networks.Sparsity in neurons enables computation saving by only operating on non-zerocomponents without hurting accuracy. Meanwhile, our method can magnifyrepresentation dimensionality and model capacity with negligible additionalcomputation cost. Experiments show that sparse representation is crucial indeep neural networks for multiple image restoration tasks, including imagesuper-resolution, image denoising, and image compression artifacts removal.Code is available at https://github.com/ychfan/nsr

Neural Sparse Representation for Image Restoration