S
Sachin Deepak Lomte
Researcher at Samsung
Publications - 4
Citations - 65
Sachin Deepak Lomte is an academic researcher from Samsung. The author has contributed to research in topics: Image restoration & Pyramid (image processing). The author has an hindex of 1, co-authored 3 publications receiving 26 citations.
Papers
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Proceedings ArticleDOI
NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results
Eduardo Perez-Pellitero,Sibi Catley-Chandar,Ales Leonardis,Radu Timofte,Xian Wang,Yong Li,Tao Wang,Fenglong Song,Zhen Liu,Wenjie Lin,Xinpeng Li,Qing Rao,Ting Jiang,Mingyan Han,Haoqiang Fan,Jian Sun,Shuaicheng Liu,Xiangyu Chen,Yihao Liu,Zhengwen Zhang,Yu Qiao,Chao Dong,Evelyn Yi Lyn Chee,Shanlan Shen,Yubo Duan,Guannan Chen,Mengdi Sun,Yan Gao,Lijie Zhang,Akhil K A,C. V. Jiji,S. M. A. Sharif,Rizwan Ali Naqvi,Mithun Biswas,Sungjun Kim,Chenjie Xia,Bowen Zhao,Zhangyu Ye,Xiwen Lu,Yanpeng Cao,Jiangxin Yang,Yanlong Cao,Green Rosh K S,Sachin Deepak Lomte,Nikhil Krishnan,B H Pawan Prasad +45 more
TL;DR: The first challenge on high-dynamic range (HDR) imaging was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021 as mentioned in this paper.
Posted Content
Spatially Variant Laplacian Pyramids for Multi-Frame Exposure Fusion
TL;DR: The proposed algorithm out performs state-of-the-art methods for image blending both qualitatively as well as quantitatively on publicly available High Dynamic Range (HDR) imaging dataset.
Proceedings ArticleDOI
Content Preserving Scale Space Network for Fast Image Restoration from Noisy-Blurry Pairs
TL;DR: This paper proposes a fast method to estimate a latent image given a pair of noisy-blurry images that uses scale space representation of the images and shows that computational efficiency can be improved by 90% compared to baseline with only a marginal drop in PSNR.
Book ChapterDOI
Spatially Variant Laplacian Pyramids for Multi-frame Exposure Fusion.
TL;DR: In this article, a spatially varying Laplacian pyramid blending method is proposed to blend images with large intensity differences, which dynamically alters the blending levels during the final stage of pyramid reconstruction based on the amount of local intensity variation.