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Sanchayan Santra - IEEE Xplore Author Profile
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Sanchayan Santra

Publications
9
Citations
255
Publications by Year
20152024
Co-Authors:
Show All Co-Authors (117)
Image of author Sanchayan Santra

Sanchayan Santra

Affiliation

Institute for Datability Science
Osaka University
Osaka, Japan

Publication Topics

Biography

Sanchayan Santra received the master’s degree from Ramakrishna Mission Vivekananda Educational and Research Institute, Belur Math, Howrah, India, and the Ph.D. degree in computer science from Indian Statistical Institute, Kolkata, India. He is currently a Researcher with the Institute for Datability Science, Osaka University, Osaka, Japan. His research interests include computational photography, computer vision, and image processing.(Based on document published on 29 January 2024).
Publications
9
Citations
255
Publications by Year
20152024
Co-Authors:
Show All Co-Authors (117)
Author's Published Works

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Significance of Anatomical Constraints in Virtual Try-On

Debapriya Roy;Sanchayan Santra;Diganta Mukherjee;Bhabatosh Chanda

IEEE Transactions on Emerging Topics in Computational Intelligence
Year: 2024 | Volume: 8, Issue: 2 | Journal Article |
The system of Virtual Try-ON (VTON) allows a user to try a product virtually. In general, a VTON system takes a clothing source and a person's image to predict the try-on output of the person in the given clothing. Although existing methods perform well for simple poses, in case of bent or crossed arms posture or when there is a significant difference between the alignment of the source clothing a...Show More

Significance of Anatomical Constraints in Virtual Try-On

Debapriya Roy;Sanchayan Santra;Diganta Mukherjee;Bhabatosh Chanda

Year: 2024 | Volume: 8, Issue: 2 | Journal Article |

Incorporating Human Body Shape Guidance for Cloth Warping in Model to Person Virtual Try-on Problems

Debapriya Roy;Sanchayan Santra;Bhabatosh Chanda

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
The world of retail has witnessed a lot of change in the last few decades and with a size of 2.4 trillion, the fashion industry is way ahead of others in this aspect. With the blessings of technology like virtual try-on (vton), now even online shoppers can virtually try a product before buying. However, the current image-based virtual try-on methods still have a long way to go when it comes to pro...Show More

Incorporating Human Body Shape Guidance for Cloth Warping in Model to Person Virtual Try-on Problems

Debapriya Roy;Sanchayan Santra;Bhabatosh Chanda

Year: 2020 | Conference Paper |

NTIRE 2019 Image Dehazing Challenge Report

Codruta O. Ancuti;Cosmin Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Tiantong Guo;Xuelu Li;Venkateswararao Cherukuri;Vishal Monga;Hao Jiang;Siyuan Yang;Yan Liu;Xiaochao Qu;Pengfei Wan;Dongwon Park;Se Young Chun;Ming Hong;Jinying Huang;Yizi Chen;Shuxin Chen;Bomin Wang;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Jing Liu;Sanchayan Santra;Ranjan Mondal;Bhabatosh Chanda;Peter Morales;Tzofi Klinghoffer;Le Manh Quan;Yong-Guk Kim;Xiao Liang;Runde Li;Jinshan Pan;Jinhui Tang;Kuldeep Purohit;Maitreya Suin;A.N. Rajagopalan;Raimondo Schettini;Simone Bianco;Flavio Piccoli;C. Cusano;Luigi Celona;Sunhee Hwang;Yu Seung Ma;Hyeran Byun;Subrahmanyam Murala;Akshay Dudhane;Harsh Aulakh;Tianxiang Zheng;Tao Zhang;Weining Qin;Runnan Zhou;Shanhu Wang;Jean-Philippe Tarel;Chuansheng Wang;Jiawei Wu

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2019 | Conference Paper |
Cited by: Papers (42)
This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze generated in an indoor or outdoor environment) and their corresponding ground truth (haze-free) images of the same scene. The dense haze has been produced using a professional haze/f...Show More

NTIRE 2019 Image Dehazing Challenge Report

Codruta O. Ancuti;Cosmin Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Tiantong Guo;Xuelu Li;Venkateswararao Cherukuri;Vishal Monga;Hao Jiang;Siyuan Yang;Yan Liu;Xiaochao Qu;Pengfei Wan;Dongwon Park;Se Young Chun;Ming Hong;Jinying Huang;Yizi Chen;Shuxin Chen;Bomin Wang;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Jing Liu;Sanchayan Santra;Ranjan Mondal;Bhabatosh Chanda;Peter Morales;Tzofi Klinghoffer;Le Manh Quan;Yong-Guk Kim;Xiao Liang;Runde Li;Jinshan Pan;Jinhui Tang;Kuldeep Purohit;Maitreya Suin;A.N. Rajagopalan;Raimondo Schettini;Simone Bianco;Flavio Piccoli;C. Cusano;Luigi Celona;Sunhee Hwang;Yu Seung Ma;Hyeran Byun;Subrahmanyam Murala;Akshay Dudhane;Harsh Aulakh;Tianxiang Zheng;Tao Zhang;Weining Qin;Runnan Zhou;Shanhu Wang;Jean-Philippe Tarel;Chuansheng Wang;Jiawei Wu

Year: 2019 | Conference Paper |

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (87)
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE data...Show More

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

Year: 2018 | Conference Paper |

Image Dehazing by Joint Estimation of Transmittance and Airlight Using Bi-Directional Consistency Loss Minimized FCN

Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (19)
Very few of the existing image dehazing methods have laid stress on the accurate restoration of color from hazy images, although it is crucial for proper removal of haze. In this paper, we are proposing a Fully Convolutional Neural Network (FCN) based image dehazing method. We have designed a network that jointly estimates scene transmittance and airlight. The network is trained using a custom des...Show More

Image Dehazing by Joint Estimation of Transmittance and Airlight Using Bi-Directional Consistency Loss Minimized FCN

Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda

Year: 2018 | Conference Paper |

Learning a Patch Quality Comparator for Single Image Dehazing

Sanchayan Santra;Ranjan Mondal;Bhabatosh Chanda

IEEE Transactions on Image Processing
Year: 2018 | Volume: 27, Issue: 9 | Journal Article |
Cited by: Papers (74)
In bad weather conditions such as fog and haze, the particles present in the atmosphere scatter incident light in different directions. As a result, the image taken under these conditions suffers from reduced visibility and lack of contrast, and as a result, it appears colorless. An image dehazing method tries to recover a haze-free portrayal of the given hazy image. In this paper, we propose a me...Show More

Learning a Patch Quality Comparator for Single Image Dehazing

Sanchayan Santra;Ranjan Mondal;Bhabatosh Chanda

Year: 2018 | Volume: 27, Issue: 9 | Journal Article |

Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination

Sanchayan Santra;Ranjan Mondal;Pranoy Panda;Nishant Mohanty;Shubham Bhuyan

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)
Year: 2017 | Conference Paper |
Cited by: Papers (4)
Haze limits the visibility of outdoor images, due to the existence of fog, smoke and dust in the atmosphere. Image dehazing methods try to recover haze-free image by removing the effect of haze from a given input image. In this paper, we present an end to end system, which takes a hazy image as its input and returns a dehazed image. The proposed method learns the mapping between a hazy image and i...Show More

Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination

Sanchayan Santra;Ranjan Mondal;Pranoy Panda;Nishant Mohanty;Shubham Bhuyan

Year: 2017 | Conference Paper |

Day/night unconstrained image dehazing

Sanchayan Santra;Bhabatosh Chanda

2016 23rd International Conference on Pattern Recognition (ICPR)
Year: 2016 | Conference Paper |
Cited by: Papers (19)
Images taken under fog or haze have their visibility reduced due to the existence of aerosols in the atmosphere. Image dehazing methods try to recover haze-free versions of these images by removing the effect of haze. Methods proposed till now are exclusively for daytime scene images or for night-time scene. The method we propose here can dehaze an image independent of whether it was captured duri...Show More

Day/night unconstrained image dehazing

Sanchayan Santra;Bhabatosh Chanda

Year: 2016 | Conference Paper |

Single image dehazing with varying atmospheric light intensity

Sanchayan Santra;Bhabatosh Chanda

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)
Year: 2015 | Conference Paper |
Cited by: Papers (7)
Images taken in bad weather conditions like haze and fog suffer from loss of contrast and color shift. The object radiance is attenuated in the atmosphere and the atmospheric light is added to the scene radiance creating a veil like semi-transparent layer called airlight. The methods proposed till now assumes that the atmospheric light is constant throughout the image domain, which may not be true...Show More

Single image dehazing with varying atmospheric light intensity

Sanchayan Santra;Bhabatosh Chanda

Year: 2015 | Conference Paper |

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