A Light Weight Convolutional Neural Network for Single Image Super-Resolution

Abstract

Abstract Recently, many convolutional neural network based models obtain remarkable performance in single-image super-resolution task by stacking more number of convolution layers. However, those models require a huge amount of network parameters which increases the computational complexity of their single image super-resolution models. Due to this, they are no longer appropriate for many real-world applications. Hence, to design a network which can obtain better super-resolution performance with less number of network parameters is always an active area of research in the computer vision community. In this paper, we propose a light weight convolutional neural network based SR model called LWSRNet for the upscaling factor x4. In LWSRNet, we introduce a novel basic block which helps to extract complex features of the given low-resolution observation. Additionally, we use a weighted L2 loss function in order to train the network which is more effective than L1 and L2 loss functions. Various experiments have been carried out to validate the proposed method and observe that the super-resolution results obtained using the proposed LWSRNet method are better than that of the other existing single image super-resolution methods. Also, the proposed LWSRNet outperforms to the recently proposed state-of-the-art methods with approximately 20% to 60% less number of training parameters.