  In recent years, Vision Transformer-based approaches for low-level vision
tasks have achieved widespread success. Unlike CNN-based models, Transformers
are more adept at capturing long-range dependencies, enabling the
reconstruction of images utilizing non-local information. In the domain of
super-resolution, Swin-transformer-based models have become mainstream due to
their capability of global spatial information modeling and their
shifting-window attention mechanism that facilitates the interchange of
information between different windows. Many researchers have enhanced model
performance by expanding the receptive fields or designing meticulous networks,
yielding commendable results. However, we observed that it is a general
phenomenon for the feature map intensity to be abruptly suppressed to small
values towards the network's end. This implies an information bottleneck and a
diminishment of spatial information, implicitly limiting the model's potential.
To address this, we propose the Dense-residual-connected Transformer (DRCT),
aimed at mitigating the loss of spatial information and stabilizing the
information flow through dense-residual connections between layers, thereby
unleashing the model's potential and saving the model away from information
bottleneck. Experiment results indicate that our approach surpasses
state-of-the-art methods on benchmark datasets and performs commendably at the
NTIRE-2024 Image Super-Resolution (x4) Challenge. Our source code is available
at https://github.com/ming053l/DRCT
