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Neural Sparse Representation for Image Restoration
Yuchen Fan · Jiahui Yu · Yiqun Mei · Yulun Zhang · Yun Fu · Ding Liu · Thomas Huang

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #402

Inspired by the robustness and efficiency of sparse representation in sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks. Our method structurally enforces sparsity constraints upon hidden neurons. The sparsity constraints are favorable for gradient-based learning algorithms and attachable to convolution layers in various networks. Sparsity in neurons enables computation saving by only operating on non-zero components without hurting accuracy. Meanwhile, our method can magnify representation dimensionality and model capacity with negligible additional computation cost. Experiments show that sparse representation is crucial in deep neural networks for multiple image restoration tasks, including image super-resolution, image denoising, and image compression artifacts removal.

Author Information

Yuchen Fan (University of Illinois at Urbana-Champaign)
Jiahui Yu (UIUC)
Yiqun Mei (University of Illinois)
Yulun Zhang (Northeastern University)
Yun Fu (Northeastern University)
Ding Liu (Bytedance AI Lab)
Thomas Huang (University of Illinois)

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