no code implementations • 9 Feb 2024 • Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
This paper tackles the problem of motion deblurring of dynamic scenes.
no code implementations • 28 Jan 2022 • Kuldeep Purohit, Srimanta Mandal, A. N. Rajagopalan
In this paper, we propose a scale recurrent SR architecture built upon units containing series of dense connections within a residual block (Residual Dense Blocks (RDBs)) that allow extraction of abundant local features from the image.
no code implementations • 28 Jan 2022 • Kuldeep Purohit, Srimanta Mandal, A. N. Rajagopalan
To enable super-resolution for multiple factors, we propose a scale-recurrent framework which reutilizes the filters learnt for lower scale factors recursively for higher factors.
no code implementations • 28 Jan 2022 • Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan
This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.
no code implementations • 1 Jan 2022 • Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
Image restoration is the task of recovering a clean image from a degraded version.
no code implementations • 1 Jan 2022 • Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
We deploy cross and self distillation techniques and discuss the need for a dedicated completion-block in encoder to achieve the distillation target.
no code implementations • 1 Jan 2022 • Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
This paper tackles the problem of dynamic scene deblurring.
no code implementations • 14 Dec 2021 • Srimanta Mandal, Kuldeep Purohit, A. N. Rajagopalan
In practice, images can contain different amounts of noise for different color channels, which is not acknowledged by existing super-resolution approaches.
no code implementations • ICCV 2021 • Kuldeep Purohit, Maitreya Suin, A. N. Rajagopalan, Vishnu Naresh Boddeti
However, we hypothesize that such spatially rigid processing is suboptimal for simultaneously restoring the degraded pixels as well as reconstructing the clean regions of the image.
no code implementations • ICCV 2021 • Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
Image inpainting methods have shown significant improvements by using deep neural networks recently.
no code implementations • 10 Nov 2020 • Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, Xianrui Luo, Ke Xian, Zijin Wu, Zhiguo Cao, Densen Puthussery, Jiji C V, Hrishikesh P S, Melvin Kuriakose, Saikat Dutta, Sourya Dipta Das, Nisarg A. Shah, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan, Saagara M B, Minnu A L, Sanjana A R, Praseeda S, Ge Wu, Xueqin Chen, Tengyao Wang, Max Zheng, Hulk Wong, Jay Zou
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
2 code implementations • 27 Sep 2020 • Majed El Helou, Ruofan Zhou, Sabine Süsstrunk, Radu Timofte, Mahmoud Afifi, Michael S. Brown, Kele Xu, Hengxing Cai, Yuzhong Liu, Li-Wen Wang, Zhi-Song Liu, Chu-Tak Li, Sourya Dipta Das, Nisarg A. Shah, Akashdeep Jassal, Tongtong Zhao, Shanshan Zhao, Sabari Nathan, M. Parisa Beham, R. Suganya, Qing Wang, Zhongyun Hu, Xin Huang, Yaning Li, Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan, Densen Puthussery, Hrishikesh P. S, Melvin Kuriakose, Jiji C. V, Yu Zhu, Liping Dong, Zhuolong Jiang, Chenghua Li, Cong Leng, Jian Cheng
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
3 code implementations • 15 Sep 2020 • Kai Zhang, Martin Danelljan, Yawei Li, Radu Timofte, Jie Liu, Jie Tang, Gangshan Wu, Yu Zhu, Xiangyu He, Wenjie Xu, Chenghua Li, Cong Leng, Jian Cheng, Guangyang Wu, Wenyi Wang, Xiaohong Liu, Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong, Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan, Xiaochuan Li, Zhiqiang Lang, Jiangtao Nie, Wei Wei, Lei Zhang, Abdul Muqeet, Jiwon Hwang, Subin Yang, JungHeum Kang, Sung-Ho Bae, Yongwoo Kim, Geun-Woo Jeon, Jun-Ho Choi, Jun-Hyuk Kim, Jong-Seok Lee, Steven Marty, Eric Marty, Dongliang Xiong, Siang Chen, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Haicheng Wang, Vineeth Bhaskara, Alex Levinshtein, Stavros Tsogkas, Allan Jepson, Xiangzhen Kong, Tongtong Zhao, Shanshan Zhao, Hrishikesh P. S, Densen Puthussery, Jiji C. V, Nan Nan, Shuai Liu, Jie Cai, Zibo Meng, Jiaming Ding, Chiu Man Ho, Xuehui Wang, Qiong Yan, Yuzhi Zhao, Long Chen, Jiangtao Zhang, Xiaotong Luo, Liang Chen, Yanyun Qu, Long Sun, Wenhao Wang, Zhenbing Liu, Rushi Lan, Rao Muhammad Umer, Christian Micheloni
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
no code implementations • CVPR 2020 • Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
Existing approaches achieve a large receptive field by increasing the number of generic convolution layers and kernel-size, but this comes at the expense of of the increase in model size and inference speed.
Ranked #27 on Image Deblurring on GoPro (using extra training data)
2 code implementations • AAAI Conference on Artificial Intelligence 2020 • Kuldeep Purohit, A. N. Rajagopalan
In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur.
1 code implementation • 18 Nov 2019 • Andreas Lugmayr, Martin Danelljan, Radu Timofte, Manuel Fritsche, Shuhang Gu, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan, Nam Hyung Joon, Yu Seung Won, Guisik Kim, Dokyeong Kwon, Chih-Chung Hsu, Chia-Hsiang Lin, Yuanfei Huang, Xiaopeng Sun, Wen Lu, Jie Li, Xinbo Gao, Sefi Bell-Kligler
For training, only one set of source input images is therefore provided in the challenge.
no code implementations • 8 Nov 2019 • Shanxin Yuan, Radu Timofte, Gregory Slabaugh, Ales Leonardis, Bolun Zheng, Xin Ye, Xiang Tian, Yaowu Chen, Xi Cheng, Zhen-Yong Fu, Jian Yang, Ming Hong, Wenying Lin, Wenjin Yang, Yanyun Qu, Hong-Kyu Shin, Joon-Yeon Kim, Sung-Jea Ko, Hang Dong, Yu Guo, Jie Wang, Xuan Ding, Zongyan Han, Sourya Dipta Das, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
no code implementations • 7 Apr 2019 • Kuldeep Purohit, Subeesh Vasu, M. Purnachandra Rao, A. N. Rajagopalan
We first propose an approach for estimation of normal of a planar scene from a single motion blurred observation.
no code implementations • 25 Mar 2019 • Kuldeep Purohit, A. N. Rajagopalan
In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur.
Ranked #26 on Image Deblurring on GoPro (using extra training data)
1 code implementation • CVPR 2019 • Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan
This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.
Ranked #37 on Image Deblurring on GoPro (using extra training data)