High Resolution Image
High-resolution image processing focuses on developing methods to effectively acquire, process, and analyze images with significantly increased detail, aiming to improve accuracy and efficiency in various applications. Current research emphasizes overcoming computational limitations of existing deep learning models (like CNNs and Transformers) when handling high-resolution inputs, focusing on novel architectures such as state-space models and diffusion models to enhance efficiency and quality. These advancements are crucial for fields like medical imaging, microscopy, and remote sensing, where high-resolution data is essential for accurate diagnosis, analysis, and decision-making. The development of efficient algorithms for high-resolution image processing is driving progress across numerous scientific disciplines and practical applications.
Papers
UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks
Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, Lei Zhu
HRSAM: Efficiently Segment Anything in High-Resolution Images
You Huang, Wenbin Lai, Jiayi Ji, Liujuan Cao, Shengchuan Zhang, Rongrong Ji
Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis
Badri N. Patro, Suhas Ranganath, Vinay P. Namboodiri, Vijay S. Agneeswaran
Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges
Andrii Kompanets, Gautam Pai, Remco Duits, Davide Leonetti, Bert Snijder