论文标题
图像修复的神经稀疏表示
Neural Sparse Representation for Image Restoration
论文作者
论文摘要
受到基于稀疏编码的图像恢复模型中稀疏表示的鲁棒性和效率的启发，我们研究了深网中神经元的稀疏性。我们的方法在结构上对隐藏的神经元产生了稀疏性约束。稀疏性限制有利于基于梯度的学习算法，并且可连接到各种网络中的卷积层。神经元中的稀疏性可以通过仅在非零组件上操作而不会损害准确性来节省计算。同时，我们的方法可以通过可忽略的额外计算成本来放大表示维度和模型容量。实验表明，对于多个图像恢复任务，稀疏表示在深层神经网络中至关重要，包括图像超分辨率，图像denoising和图像压缩伪像去除。代码可从https://github.com/ychfan/nsr获得
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. Code is available at https://github.com/ychfan/nsr
