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
NVILA: Efficient Frontier Visual Language Models
Zhijian Liu, Ligeng Zhu, Baifeng Shi, Zhuoyang Zhang, Yuming Lou, Shang Yang, Haocheng Xi, Shiyi Cao, Yuxian Gu, Dacheng Li, Xiuyu Li, Yunhao Fang, Yukang Chen, Cheng-Yu Hsieh, De-An Huang, An-Chieh Cheng, Vishwesh Nath, Jinyi Hu, Sifei Liu, Ranjay Krishna, Daguang Xu, Xiaolong Wang, Pavlo Molchanov, Jan Kautz, Hongxu Yin, Song Han, Yao Lu
Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image
Shuang Xu, Zixiang Zhao, Haowen Bai, Chang Yu, Jiangjun Peng, Xiangyong Cao, Deyu Meng
Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection
Serhii Svystun, Oleksandr Melnychenko, Pavlo Radiuk, Oleg Savenko, Anatoliy Sachenko, Andrii Lysyi
TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting
Muhammad Hamza Sharif, Dmitry Demidov, Asif Hanif, Mohammad Yaqub, Min Xu
Leveraging generative models to characterize the failure conditions of image classifiers
Adrien LeCoz, Stéphane Herbin, Faouzi Adjed