In recent years, Vision Transformer-based applications to 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 information from non-local areas. In the
domain of super-resolution, Swin-transformer-based approaches have become
mainstream due to their capacity to capture global spatial information and
their shifting-window attention mechanism that facilitates the interchange of
information between different windows. Many researchers have enhanced image
quality and network efficiency by expanding the receptive field or designing
complex networks, yielding commendable results. However, we observed that
spatial information tends to diminish during the forward propagation process
due to increased depth, leading to a loss of spatial information and,
consequently, limiting the model's potential. To address this, we propose the
Dense-residual-connected Transformer (DRCT), aimed at mitigating the loss of
spatial information through dense-residual connections between layers, thereby
unleashing the model's potential and enhancing performance. Experiment results
indicate that our approach is not only straightforward but also achieves
remarkable efficiency, surpassing state-of-the-art methods and performing
commendably at NTIRE2024.

通过在层间添加密集残差连接以减少空间信息的丢失，我们提出了 Dense-residual-connected Transformer (DRCT) 模型，提高了视觉变换和超分辨率任务的性能。