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Kalpesh Prajapati
Researcher at Sardar Vallabhbhai National Institute of Technology, Surat
Publications - 16
Citations - 238
Kalpesh Prajapati is an academic researcher from Sardar Vallabhbhai National Institute of Technology, Surat. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 5, co-authored 15 publications receiving 80 citations.
Papers
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Proceedings ArticleDOI
NTIRE 2021 Learning the Super-Resolution Space Challenge
Andreas Lugmayr,Martin Danelljan,Radu Timofte,Christoph Busch,Yang Chen,Jian Cheng,Vishal Chudasama,Ruipeng Gang,Shangqi Gao,Kun Gao,Laiyun Gong,Qingrui Han,Chao Huang,Zhi Jin,Younghyun Jo,Seon Joo Kim,Younggeun Kim,Seungjun Lee,Yuchen Lei,Chu-Tak Li,Chenghua Li,Ke Li,Zhi-Song Liu,Youming Liu,Nan Nan,Seung-Ho Park,Heena Patel,Shichong Peng,Kalpesh Prajapati,Haoran Qi,Kiran B. Raja,Raghavendra Ramachandra,Wan-Chi Siu,Donghee Son,Ruixia Song,Kishor P. Upla,Li-Wen Wang,Yatian Wang,Junwei Wang,Qianyu Wu,Xinhua Xu,Sejong Yang,Zhen Yuan,Liting Zhang,Huanrong Zhang,Junkai Zhang,Yifan Zhang,Zhenzhou Zhang,Hangqi Zhou,Aichun Zhu,Xiahai Zhuang,Jiaxin Zou +51 more
TL;DR: The NTIRE 2021 challenge as mentioned in this paper addressed the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image.
Proceedings ArticleDOI
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov,Cheng-Ming Chiang,Hsien-Kai Kuo,Anastasia Sycheva,Radu Timofte,Min-Hung Chen,Man-Yu Lee,Yu-Syuan Xu,Yu Tseng,Shusong Xu,Jin Guo,Chao-Hung Chen,Ming-Chun Hsyu,Wen-Chia Tsai,Chao-Wei Chen,Grigory Malivenko,Minsu Kwon,Myungje Lee,Jaeyoon Yoo,Changbeom Kang,Shinjo Wang,Zheng Shaolong,Hao Dejun,Xie Fen,Feng Zhuang,Yipeng Ma,Jingyang Peng,Tao Wang,Fenglong Song,Chih-Chung Hsu,Kwan-Lin Chen,Mei-Hsuang Wu,Vishal Chudasama,Kalpesh Prajapati,Heena Patel,Anjali Sarvaiya,Kishor P. Upla,Kiran B. Raja,Raghavendra Ramachandra,Christoph Busch,Etienne de Stoutz +40 more
TL;DR: In this article, an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs was developed.
Proceedings ArticleDOI
NTIRE 2021 NonHomogeneous Dehazing Challenge Report
Codruta Orniana Ancuti,Cosmin Ancuti,Florin-Alexandru Vasluianu,Radu Timofte,Minghan Fu,Huan Liu,Yankun Yu,Jun Chen,Keyan Wang,Jerome Chang,Xiyao Wang,Jing Liu,Yi Xu,Xinjian Zhang,Minyi Zhao,Shuigeng Zhou,Tianyi Chen,Jiahui Fu,Wentao Jiang,Chen Gao,Si Liu,Yudong Wang,Jichang Guo,Chongyi Li,Qixin Yan,Sida Zheng,Syed Waqas Zamir,Aditya Arora,Akshay Dudhane,Salman Khan,Munawar Hayat,Fahad Shahbaz Khan,Ling Shao,Haichuan Zhang,Tiantong Guo,Vishal Monga,Wenjin Yang,Jin Lin,Xiaotong Luo,Guowen Huang,Shuxin Chen,Yanyun Qu,Kele Xu,Lehan Yang,Pengliang Sun,Xuetong Niu,Junjun Zheng,Xiaotong Ruan,Yunfeng Wang,Jiang Yang,Zhipeng Luo,Sai Wang,Zhenyu Xu,Xiaochun Cao,Jun Luo,Zhuoran Zheng,Wenqi Ren,Tao Wang,Yiqun Chen,Cong Leng,Chenghua Li,Jian Cheng,Chang-Sung Sung,Jun-Cheng Chen,Eunsung Jo,Jae-Young Sim,Geethu M M,Akhil K A,Sreeni K G,Jeena R S,Joseph Zacharias,Chippy M Manu,Zexi Huang,Baofeng Zhang,Yiwen Zhang,Jindong Li,Mianjie Chen,Quan Xiao,Qingchao Su,Lihua Han,Yanting Huang,Kalpesh Prajapati,Vishal Chudasama,Heena Patel,Anjali Sarvaiya,Kishor P. Upla,Kiran B. Raja,Raghavendra Ramachandra,Christoph Busch,Hongyuan Jing,Zilong Huang,Yiran Fu,Haoqiang Wu,Quanxing Zha,Zhiwei Zhu,Hejun Lv +95 more
TL;DR: The results of the NTIRE 2021 Challenge on Non-Homogeneous Dehazing as mentioned in this paper have been evaluated on a novel dataset that consists of additional 35 pairs of real haze free and non-homogeneous hazy images recorded outdoor.
Proceedings ArticleDOI
Unsupervised Single Image Super-Resolution Network (USISResNet) for Real-World Data Using Generative Adversarial Network
Kalpesh Prajapati,Vishal Chudasama,Heena Patel,Kishor P. Upla,Raghavendra Ramachandra,Kiran B. Raja,Christoph Busch +6 more
TL;DR: This work proposes a new SR approach to mitigate such an issue using unsupervised learning in Generative Adversarial Network (GAN) framework - USISResNet and introduces a new loss function based on the Mean Opinion Score (MOS) to provide high quality SR image for perceptual inspection.
Proceedings ArticleDOI
TherISuRNet - A Computationally Efficient Thermal Image Super-Resolution Network
Vishal Chudasama,Heena Patel,Kalpesh Prajapati,Kishor P. Upla,Raghavendra Ramachandra,Kiran B. Raja,Christoph Busch +6 more
TL;DR: This paper proposes a Super-Resolution (SR) of thermal images using a deep neural network architecture which is computationally efficient for different upscaling factors such as ×2, ×3 and ×4 and consists of different modules for low and high-frequency feature extraction along with upsampling blocks.