AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results

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.

Image Super-Resolution

PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

no code implementations • 3 Oct 2018 • Andrey Ignatov, Radu Timofte, Thang Van Vu, Tung Minh Luu, Trung X. Pham, Cao Van Nguyen, Yongwoo Kim, Jae-Seok Choi, Munchurl Kim, Jie Huang, Jiewen Ran, Chen Xing, Xingguang Zhou, Pengfei Zhu, Mingrui Geng, Yawei Li, Eirikur Agustsson, Shuhang Gu, Luc van Gool, Etienne de Stoutz, Nikolay Kobyshev, Kehui Nie, Yan Zhao, Gen Li, Tong Tong, Qinquan Gao, Liu Hanwen, Pablo Navarrete Michelini, Zhu Dan, Hu Fengshuo, Zheng Hui, Xiumei Wang, Lirui Deng, Rang Meng, Jinghui Qin, Yukai Shi, Wushao Wen, Liang Lin, Ruicheng Feng, Shixiang Wu, Chao Dong, Yu Qiao, Subeesh Vasu, Nimisha Thekke Madam, Praveen Kandula, A. N. Rajagopalan, Jie Liu, Cheolkon Jung

This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.

Image Enhancement Image Super-Resolution

AIM 2019 Challenge on Image Demoireing: Methods and Results

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).

Demoire Image Manipulation

AIM 2020 Challenge on Rendering Realistic Bokeh

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.

Bokeh Effect Rendering

Unsupervised Domain-Specific Deblurring using Scale-Specific Attention

no code implementations • 12 Dec 2021 • Praveen Kandula, Rajagopalan. A. N

Different ablation studies show that our coarse-to-fine mechanism outperforms end-to-end unsupervised models and SAAM is able to attend better compared to attention models used in literature.

Deblurring Image Deblurring

Unsupervised haze removal from underwater images

no code implementations • 5 Jun 2023 • Praveen Kandula, A. N. Rajagopalan

Several supervised networks exist that remove haze information from underwater images using paired datasets and pixel-wise loss functions.

Disentanglement

Zero shot framework for satellite image restoration

no code implementations • 5 Jun 2023 • Praveen Kandula, A. N. Rajagopalan

We then propose the use of knowledge distillation to train a restoration network using the generated image pairs.

Disentanglement Image Restoration +1

Unsupervised network for low-light enhancement

no code implementations • 5 Jun 2023 • Praveen Kandula, Maitreya Suin, A. N. Rajagopalan

Different ablation studies show the importance of PAM and CIN in improving the visible quality of the image.