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Amin Kheradmand
Researcher at Dalian Maritime University
Publications - 13
Citations - 388
Amin Kheradmand is an academic researcher from Dalian Maritime University. The author has contributed to research in topics: Image processing & Laplacian matrix. The author has an hindex of 7, co-authored 13 publications receiving 285 citations. Previous affiliations of Amin Kheradmand include Dolby Laboratories & University of California, Santa Cruz.
Papers
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Journal ArticleDOI
A General Framework for Regularized, Similarity-Based Image Restoration
Amin Kheradmand,Peyman Milanfar +1 more
TL;DR: An iterative graph-based framework for image restoration based on a new definition of the normalized graph Laplacian, which comprises of outer and inner iterations, where in each outer iteration, the similarity weights are recomputed using the previous estimate and the updated objective function is minimized using inner conjugate gradient iterations.
Posted Content
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr,Martin Danelljan,Radu Timofte,Namhyuk Ahn,Dongwoon Bai,Jie Cai,Yun Cao,Junyang Chen,Kaihua Cheng,Se Young Chun,Wei Deng,Mostafa El-Khamy,Chiu Man Ho,Xiaozhong Ji,Amin Kheradmand,Gwantae Kim,Hanseok Ko,Kanghyu Lee,Jungwon Lee,Hao Li,Ziluan Liu,Zhi-Song Liu,Shuai Liu,Yunhua Lu,Zibo Meng,Pablo Navarrete Michelini,Christian Micheloni,Kalpesh Prajapati,Haoyu Ren,Yong Hyeok Seo,Wan-Chi Siu,Kyung-Ah Sohn,Ying Tai,Rao Muhammad Umer,Shuangquan Wang,Huibing Wang,Timothy Haoning Wu,Haoning Wu,Biao Yang,Fuzhi Yang,Jaejun Yoo,Tongtong Zhao,Yuanbo Zhou,Haijie Zhuo,Ziyao Zong,Xueyi Zou +45 more
TL;DR: The NTIRE 2020 challenge addresses the real world setting, where paired true high and low-resolution images are unavailable, and the ultimate goal is to achieve the best perceptual quality, evaluated using a human study.
Proceedings ArticleDOI
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr,Martin Danelljan,Radu Timofte,Namhyuk Ahn,Dongwoon Bai,Jie Cai,Yun Cao,Junyang Chen,Kaihua Cheng,Se Young Chun,Wei Deng,Mostafa El-Khamy,Chiu Man Ho,Xiaozhong Ji,Amin Kheradmand,Gwantae Kim,Hanseok Ko,Kanghyu Lee,Jungwon Lee,Hao Li,Ziluan Liu,Zhi-Song Liu,Shuai Liu,Yunhua Lu,Zibo Meng,Pablo Navarrete Michelini,Christian Micheloni,Kalpesh Prajapati,Haoyu Ren,Yong Hyeok Seo,Wan-Chi Siu,Kyung-Ah Sohn,Ying Tai,Rao Muhammad Umer,Shuangquan Wang,Huibing Wang,Timothy Haoning Wu,Haoning Wu,Biao Yang,Fuzhi Yang,Jaejun Yoo,Tongtong Zhao,Yuanbo Zhou,Haijie Zhuo,Ziyao Zong,Xueyi Zou +45 more
TL;DR: The NTIRE 2020 challenge as discussed by the authors addressed the real world setting, where paired true high and low-resolution images are unavailable, for training, only one set of source input images is provided along with a set of unpaired high-quality target images.
Proceedings ArticleDOI
A general framework for kernel similarity-based image denoising
Amin Kheradmand,Peyman Milanfar +1 more
TL;DR: A general regularization framework for image denoising based on the spectral properties of Laplacian matrices is presented, which provides a better understanding of enhancement mechanisms in self similarity-based methods, which can be used for their further improvement.
Proceedings ArticleDOI
Real-World Super-Resolution using Generative Adversarial Networks
TL;DR: This paper designs a generic training set with the LR images generated by various degradation models from a set of HR images, and achieves good perceptual quality by super resolving theLR images whose degradation was caused by unknown image processing artifacts.