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
LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images
Ruyi Xu, Yuan Yao, Zonghao Guo, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, Maosong Sun, Gao Huang
CasSR: Activating Image Power for Real-World Image Super-Resolution
Haolan Chen, Jinhua Hao, Kai Zhao, Kun Yuan, Ming Sun, Chao Zhou, Wei Hu