MB-TaylorFormer V2: Improved Multi-branch Linear Transformer . . . Experimental results across diverse image restoration benchmarks demonstrate that MB-TaylorFormer V2 achieves state-of-the-art performance in multiple image restoration tasks, such as image dehazing, deraining, desnowing, motion deblurring, and denoising, with very little computational overhead
Fully-Connected Transformer for Multi-Source Image Fusion The proposed mechanism employs multilinear algebra to drive the development of a novel fully-connected self-attention (FCSA) method to fully exploit local and non-local domain-specific correlations among multi-source images
Transformer For Medical Image Analysis - GitHub Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives Medical image analysis, 102762 Last updated: 03 17 2022 X-ray CT Colonoscopy Pathology Colonoscopy Histology Is it Time to Replace CNNs with Transformers for Medical Images? Mammograms
A hybrid network of CNN and transformer for subpixel shifting . . . In the second stage, we introduce an SP-MISR network model, leveraging a hybrid of convolutional neural networks and transformer architecture, to utilize the subpixel shift information for generating HR images from SPS images
Multi-granularity Transformer for Image Super-resolution In this work we propose a novel transformer-based method, named Multi-granularity Transformer (MugFormer), which efficiently aggregate both local and global informa-tion in an image to enhance details while maintaining relatively low computational cost