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MCSNet: Multi-Channel Sharing Network for Single Image Super-Resolution

Due to the rapid development of convolutional neural networks (CNNs) architecture, single-image super-resolution (SISR) performance and processing speed increased. However, CNN-based architectures have a high computational cost, more processing time, and reconstructed results are still unsatisfactory. Furthermore, CNN architectures depend on the single-channel network architecture to reconstructing the high-resolution (HR) image, which also creates problems during the training, extracted features are not received at the last end layer. To report these issues, we suggested a Multi-Channel Sharing Network for Single Image Super-Resolution (MCSNet). In addition, the multi-channel sharing technique is used to extract and share the feature information through different routes, which is very important to resolve the SISR problem. Numerous quantitative and qualitative experimental findings demonstrate that our suggested method outperforms existing state-of-the-art approaches in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), computing complexity, and perceptual quality. Especially on challenging enlargement scale factor 8x, our proposed method improves the overall average PSNR on all test datasets, including Mangal09, UrbanlOO, Set14, and Set5, by 2.04dB with the baseline method.