A Compact-Structured Convolutional Neural Network for Single Image Denoising and Super-Resolution

Abstract

In this work, a simple and compact-structured convolutional neural network (CNN) entitled as Denoising and Super-Resolution (DnSR) network for single image denoising and super resolution (SISR) is presented. Using sparsity property (a smaller number of large intensity values and a larger number of small intensity values), the training of DnSR is made to converge early. We obtain the sparsity by learning residuals of image pixels instead of learning raw image pixels particularly for SR. We use a smaller receptive field of the image patch for faster training. We use gradient clipping to train DnSR net with an extremely large learning rate to overcome gradient inconsistency during training. An end-to-end mapping is done by learning the complex mapping between low-resolution (LR) image patches and the corresponding residual of high resolution (∆HR) image patches using DnSR net. The proposed DnSR net uses a smaller number of parameters (approximately 3 times lesser) than the existing state-of-the-art (SOTA) SRCNN method. The proposed DnSR provides superior reconstruction accuracy with less artifacts when compared to other SOTA methods. The performance of the DnSR is assessed qualitatively and quantitatively using different image quality metrics.