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Chia-Hsiang Lin
Researcher at National Cheng Kung University
Publications - 49
Citations - 830
Chia-Hsiang Lin is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 11, co-authored 39 publications receiving 485 citations. Previous affiliations of Chia-Hsiang Lin include Instituto Superior Técnico & National Tsing Hua University.
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Book
Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications
TL;DR: Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications as discussed by the authors provides fundamental background knowledge of convex optimization, while striking a balance between mathematical theory and applications in signal processing and communications.
Proceedings ArticleDOI
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr,Nam Hyung Joon,Yu Seung Won,Guisik Kim,Dokyeong Kwon,Chih-Chung Hsu,Chia-Hsiang Lin,Yuanfei Huang,Xiaopeng Sun,Wen Lu,Jie Li,Martin Danelljan,Xinbo Gao,Sefi Bell-Kligler,Assaf Shocher,Michal Irani,Radu Timofte,Manuel Fritsche,Shuhang Gu,Kuldeep Purohit,Praveen Kandula,Maitreya Suin,A N Rajagoapalan +22 more
TL;DR: The AIM 2019 challenge on real world super-resolution addresses the real world setting, where paired true high and low-resolution images are unavailable, and aims to advance the state-of-the-art and provide a standard benchmark for this newly emerging task.
Journal ArticleDOI
A Convex Optimization-Based Coupled Nonnegative Matrix Factorization Algorithm for Hyperspectral and Multispectral Data Fusion
TL;DR: Besides the commonly used sparsity-promoting regularization, this work incorporates the well-known sum-of-squared-distances regularizer, which serves as a convex surrogate of the volume of the simplex of materials’ spectral signature vectors, into the CNMF criterion, thereby leading to a conveX formulation of the fusion problem.
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Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No-Pure-Pixel Case
TL;DR: In this paper, the perfect end-member identifiability of the minimum volume enclosing simplex (MVES) algorithm is studied under the noiseless case. And the theoretical results are supported by numerical simulation results.
Journal ArticleDOI
Regularization Parameter Selection in Minimum Volume Hyperspectral Unmixing
TL;DR: This paper puts some popular linear HU formulations under a unifying framework, which involves a data-fitting term and an MV-based regularization term, and collectively solve it via a nonconvex optimization by exploiting the fact that a too large parameter overshrinks the volume of the simplex defined by the endmembers.