NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
- URL: http://arxiv.org/abs/2404.14248v1
- Date: Mon, 22 Apr 2024 15:01:12 GMT
- Title: NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
- Authors: Xiaoning Liu, Zongwei Wu, Ao Li, Florin-Alexandru Vasluianu, Yulun Zhang, Shuhang Gu, Le Zhang, Ce Zhu, Radu Timofte, Zhi Jin, Hongjun Wu, Chenxi Wang, Haitao Ling, Yuanhao Cai, Hao Bian, Yuxin Zheng, Jing Lin, Alan Yuille, Ben Shao, Jin Guo, Tianli Liu, Mohao Wu, Yixu Feng, Shuo Hou, Haotian Lin, Yu Zhu, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang, Qingsen Yan, Wenbin Zou, Weipeng Yang, Yunxiang Li, Qiaomu Wei, Tian Ye, Sixiang Chen, Zhao Zhang, Suiyi Zhao, Bo Wang, Yan Luo, Zhichao Zuo, Mingshen Wang, Junhu Wang, Yanyan Wei, Xiaopeng Sun, Yu Gao, Jiancheng Huang, Hongming Chen, Xiang Chen, Hui Tang, Yuanbin Chen, Yuanbo Zhou, Xinwei Dai, Xintao Qiu, Wei Deng, Qinquan Gao, Tong Tong, Mingjia Li, Jin Hu, Xinyu He, Xiaojie Guo, Sabarinathan, K Uma, A Sasithradevi, B Sathya Bama, S. Mohamed Mansoor Roomi, V. Srivatsav, Jinjuan Wang, Long Sun, Qiuying Chen, Jiahong Shao, Yizhi Zhang, Marcos V. Conde, Daniel Feijoo, Juan C. Benito, Alvaro GarcĂa, Jaeho Lee, Seongwan Kim, Sharif S M A, Nodirkhuja Khujaev, Roman Tsoy, Ali Murtaza, Uswah Khairuddin, Ahmad 'Athif Mohd Faudzi, Sampada Malagi, Amogh Joshi, Nikhil Akalwadi, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudenagudi, Wenyi Lian, Wenjing Lian, Jagadeesh Kalyanshetti, Vijayalaxmi Ashok Aralikatti, Palani Yashaswini, Nitish Upasi, Dikshit Hegde, Ujwala Patil, Sujata C, Xingzhuo Yan, Wei Hao, Minghan Fu, Pooja choksy, Anjali Sarvaiya, Kishor Upla, Kiran Raja, Hailong Yan, Yunkai Zhang, Baiang Li, Jingyi Zhang, Huan Zheng,
- Abstract summary: This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions.
A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions.
- Score: 140.18794489156704
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
Related papers
- NTIRE 2024 Challenge on Night Photography Rendering [85.05686186795512]
The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions.
The speed of algorithms was also measured alongside the quality of their output.
The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering.
arXiv Detail & Related papers (2024-06-18T18:56:25Z) - Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey [65.2234198408208]
This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results.
The goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur.
arXiv Detail & Related papers (2024-04-24T21:51:01Z) - Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey [43.57460813800406]
This paper reviews the NTIRE 2024 Portrait Quality Assessment Challenge, highlighting the proposed solutions and results.
This challenge aims to obtain an efficient deep neural network capable of estimating the perceptual quality of real portrait photos.
arXiv Detail & Related papers (2024-04-17T08:15:25Z) - NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results [126.78130602974319]
This paper reviews the NTIRE 2024 challenge on image super-resolution ($times$4)
The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs.
The aim of the challenge is to obtain designs/solutions with the most advanced SR performance.
arXiv Detail & Related papers (2024-04-15T13:45:48Z) - NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results [116.77874476501913]
Given a noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times higher resolution.
The challenge contained two tracks; Track 1 evaluating on synthetically generated data, and Track 2 using real-world bursts from mobile camera.
The top-performing methods set a new state-of-the-art for the burst super-resolution task.
arXiv Detail & Related papers (2021-06-07T17:55:28Z) - NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and
Results [240.4967106943687]
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution.
The challenge task was to super-resolve an input image with a magnification factor 16.
The track had 280 registered participants, and 19 teams submitted the final results.
arXiv Detail & Related papers (2020-05-03T11:30:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.