Dense Residual Connected Transformer

Dense Residual Connected Transformers (DRCTs) are a novel class of neural network architectures combining the strengths of Transformers and residual dense connections to improve performance on various low-level computer vision tasks, such as image super-resolution, denoising, and multi-view stereo. Current research focuses on designing efficient DRCT models, often incorporating enhanced attention mechanisms and grid-based structures, to address computational limitations while capturing both local and global image features. These advancements demonstrate improved accuracy and efficiency compared to traditional Convolutional Neural Network (CNN) approaches, impacting fields like medical imaging and adverse weather image restoration.