no code implementations • 12 Dec 2023 • Yo-Yu Lai, Chia-Hsiang Lin, Zi-Chao Leng
The deep learning model Transformer has achieved remarkable success in the hyperspectral image (HSI) restoration tasks by leveraging Spectral and Spatial Self-Attention (SA) mechanisms.
no code implementations • 12 Sep 2021 • Chia-Hsiang Lin, Yi-Chun Hung, Feng-Yu Wang, Shang-Hua Yang
Terahertz (THz) technology has been a great candidate for applications, including pharmaceutic analysis, chemical identification, and remote sensing and imaging due to its non-invasive and non-destructive properties.
no code implementations • 20 Nov 2019 • Chih-Chung Hsu, Chia-Hsiang Lin
Recently, an enhanced super-resolution based on generative adversarial network (ESRGAN) has achieved excellent performance in terms of both qualitative and quantitative quality of the reconstructed high-resolution image.
1 code implementation • 18 Nov 2019 • Andreas Lugmayr, Martin Danelljan, Radu Timofte, Manuel Fritsche, Shuhang Gu, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan, Nam Hyung Joon, Yu Seung Won, Guisik Kim, Dokyeong Kwon, Chih-Chung Hsu, Chia-Hsiang Lin, Yuanfei Huang, Xiaopeng Sun, Wen Lu, Jie Li, Xinbo Gao, Sefi Bell-Kligler
For training, only one set of source input images is therefore provided in the challenge.
no code implementations • 9 Aug 2017 • Chia-Hsiang Lin, Ruiyuan Wu, Wing-Kin Ma, Chong-Yung Chi, Yue Wang
This maximum volume inscribed ellipsoid (MVIE) idea has not been attempted in prior literature, and we show a sufficient condition under which the MVIE framework guarantees exact recovery of the factors.
no code implementations • 20 Jun 2014 • Chia-Hsiang Lin, Wing-Kin Ma, Wei-Chiang Li, Chong-Yung Chi, ArulMurugan Ambikapathi
In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known to be powerful in enabling simple and effective blind HU solutions.