Supplementary Material for Neural Sparse Representation for Image Restoration Y uchen Fan, Jiahui Y u, Yiqun Mei, Y ulun Zhang, Y un Fu, Ding Liu

In Eq. 9, we reduce the soft sparsity constraints to the weighted sum of convolution kernels. Here, we will give a detailed proof of the derivation process. Formally, i denotes the index of the activated group, then s.t. Figure 1: Unified network structure for image restoration (left). In our paper, we claim the additional complexity of our method is negligible. As shown in Figure 1, the structure is stacked by multiple residual blocks and additional convolution layers for input and output.