Arbitrary Scale Super-Resolution Neural Network Based on Residual Channel-Spatial Attention

Abstract

In recent years, the performance of convolutional neural networks in single-image superresolution has improved significantly. However, most state-of-the-art models address the super-resolution problem for specific scale factors. In this paper, we propose a convolutional neural network for arbitrary scale super-resolution. Specifically, given a range of scale factors, the proposed model can generate superresolution images with non-integer scale factors within the range. The proposed model incorporates a channel-spatial attention block in which the scale factor is also provided. This module recovers the most relevant information from the low-resolution image given the scale factor and enhances the upsampled image before generating the high-resolution target image. This channel attention block allows learning the channel and spatial dependencies. Additionally, we incorporate global residual learning so that the model recovers the details of an upsampled low-resolution image by interpolation. We evaluated the proposed method through extensive experiments on widely used benchmark datasets for single-image super-resolution. In order to assess the performance of the model, we used the peak signal-to-noise ratio and the structural similarity index measure. The proposed model achieves an average of 35.36, 31.78, 29.62 for peak signalto-noise ratio, and 0.9410, 0.8828, 0.8334 for structural similarity index measure for the standard evaluation scale factors ×2, ×3, ×4, respectively. The experimental results show a better performance of the proposed model over other state-of-the-art models for arbitrary scale super-resolution, and are competitive with models trained for specific scale factors.