A Compact Deep Neural Network for Single Image Super-Resolution

Convolutional neural network (CNN) has recently been applied into single image super-resolution (SISR) task. But the applied CNN models are increasingly cumbersome which will cause heavy memory and computational burden when deploying in realistic applications. Besides, existing CNNs for SISR have trouble in handling different scales information with same kernel size. In this paper, we propose a compact deep neural network (CDNN) to (1) reduce the amount of model parameters (2) decrease computational operations and (3) process different scales information. We devise two kinds of channel-wise scoring units (CSU), including adaptive channel-wise scoring unit (ACSU) and constant channel-wise scoring unit (CCSU), which act as judges to score for different channels. With further sparsity regularization imposed on CSUs and ensuing pruning of low-score channels, we can achieve considerable storage saving and computation simplification. In addition, the CDNN contains a dense inception structure, the convolutional kernels of which are in different sizes. This enables the CDNN to cope with different scales information in one natural image. We demonstrate the effectiveness of CSUs, dense inception on benchmarks and the proposed CDNN has superior performance over other methods.