The paper "DRCT: Saving Image Super-Resolution away from Information Bottleneck" by Chih-Chung Hsu, Chia-Ming Lee, and Yi-Shiuan Chou addresses the issue of information bottleneck in Vision Transformer-based models used for image super-resolution (SISR). The authors observe that as the network depth increases, the intensity of feature maps decreases sharply, leading to a loss of spatial information and potentially limiting the model's performance. To mitigate this, they propose the Dense-residual-connected Transformer (DRCT), which incorporates dense-residual connections within residual blocks to stabilize the information flow and enhance the receptive field. This approach allows the model to capture long-range dependencies more effectively without increasing computational complexity. Experimental results on benchmark datasets and the NTIRE-2024 Image Super-Resolution (x4) Challenge demonstrate that DRCT outperforms state-of-the-art methods, achieving superior performance with simpler model architectures. The paper also discusses related works, problem statements, and the methodology behind DRCT, including its architecture and training strategies.The paper "DRCT: Saving Image Super-Resolution away from Information Bottleneck" by Chih-Chung Hsu, Chia-Ming Lee, and Yi-Shiuan Chou addresses the issue of information bottleneck in Vision Transformer-based models used for image super-resolution (SISR). The authors observe that as the network depth increases, the intensity of feature maps decreases sharply, leading to a loss of spatial information and potentially limiting the model's performance. To mitigate this, they propose the Dense-residual-connected Transformer (DRCT), which incorporates dense-residual connections within residual blocks to stabilize the information flow and enhance the receptive field. This approach allows the model to capture long-range dependencies more effectively without increasing computational complexity. Experimental results on benchmark datasets and the NTIRE-2024 Image Super-Resolution (x4) Challenge demonstrate that DRCT outperforms state-of-the-art methods, achieving superior performance with simpler model architectures. The paper also discusses related works, problem statements, and the methodology behind DRCT, including its architecture and training strategies.