Multi-FusNet of Cross Channel Network for Image Super-Resolution

Abstract

Image Super-resolution (SR) has gained considerable attention in artificial intelligence (AI) research and image-based applications. Recent deep learning-based SR models have demonstrated remarkable accuracy and perceptual quality in the resulting images. However, the computational cost and model parameters are the most challenging limitations in real-world applications. Additionally, designing an efficient and lightweight SR algorithm to improve the perceptual quality of the SR images is a critical issue. According to these considerations, we propose a Multi-FusNet of Cross Channel Network (MFCC) network by modeling a multipath residual network, named multi-RG, with cross-filtering fusion. Additionally, a pixel shuffling fusion technique is used to fuse low-level features into the up-sampled features of the multi-RG. The experimental results show the comparison of the proposed MFCC to the state-of-the-art SR models. The proposed method significantly reduces the number of network parameters (8.4 times compared to RCAN) while preserving the visual quality of the result and achieving the best PSNR value compared to the other state-of-the-art methods.