CANS: Combined Attention Network for Single Image Super-Resolution

Single image super-resolution (SISR) is a rapidly advancing area that has attracted considerable interest in recent years, largely due to the successful use of deep convolutional neural networks (CNNs). This growth can be attributed to several factors that highlight the transformative impact of CNNs on image processing tasks. These networks have demonstrated considerable improvements in image quality metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). However, despite their impressive performance, several challenges remain, particularly regarding computational efficiency and resource requirements. Moreover, SISR models focus on a single pathway for feature extraction, using these features to reconstruct the high-resolution (HR) output image. On the other hand, these approaches obtain improved performance, creating issues for deeper and denser networks at the later end layers. Later end layers do not receive complete feature information and work as a dead layer. We present a novel architecture that combines all pathways with different attention blocks, called the Combined Attention Network for Single Image Super-Resolution (CANS), to tackle these challenges. In this approach, we employed multiple blocks such as the Shallow Block-based Network (SBN) path, Deep Block-based Network (DeBN) path, and Dense Block-based Network (DBN) path to extract the local, global, and dense features for reconstructing the visually pleasing HR image. Additionally, we have developed a novel method for skip connection learning that integrates both local and global residual learning with an attention block to address the vanishing gradient problem and improve convergence speed. Furthermore, we introduce the Triplet Channel Attention Network (TCAN) Block, which leverages hierarchical features from the original low-resolution images to reconstruct high-quality high-resolution features. Extensive experimental results demonstrate that CANS effectively tackles traditional SISR challenges while delivering remarkable performance with lower computational costs compared to existing state-of-the-art methods.