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

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

Blog Article

Image Super-resolution (SR) has gained considerable attention in artificial intelligence (AI) research and image-based scooters 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 Pokers result and achieving the best PSNR value compared to the other state-of-the-art methods.