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

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... mber 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 ×4. 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 L 2 loss function in order to train the network which is more effective than L 1 and L 2 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.