Abstract:
Recently, single image super-resolution (SISR), aiming to preserve the lost structural and textural information from the input low resolution image, has witnessed huge demand from the videos and graphics
industries. The exceptional success of convolution neural networks (CNNs), has absolutely revolutionized
the field of SISR. However, for most of the CNN-based SISR methods, excessive memory consumption in
terms of parameters and flops, hinders their application in low-computing power devices. Moreover, different state-of-the-art SR methods collect different features, by treating all the pixels contributing equally
to the performance of the network. In this paper, we take into consideration both the performance and
the reconstruction efficiency, and propose a Light-weight multi-scale attention residual network (MSARNet) for SISR. The proposed MSAR-Net consists of stack of multi-scale attention residual (MSAR) blocks
for feature refinement, and an up and down-sampling projection (UDP) block for edge refinement of the
extracted multi-scale features. These blocks are capable of effectively exploiting the multi-scale edge information, without increasing the number of parameters. Specially, we design our network in progressive
fashion, for substituting the large scale factors (× 4) combinations, with small scale factor (×2) combinations, and thus gradually exploit the hierarchical information. In parallel, for modulation of multi-scale
features in global and local manners, channel and spatial attention in MSAR block is being used. Visual
results and quantitative metrics of PSNR and SSIM exhibit the accuracy of the proposed approach on synthetic benchmark super-resolution datasets. The experimental analysis shows that the proposed approach
outperforms the other existing methods for SISR in terms of memory footprint, inference time and visual
quality.
