NTIRE 2020 Challenge on NonHomogeneous Dehazing
- URL: http://arxiv.org/abs/2005.03457v1
- Date: Thu, 7 May 2020 13:29:56 GMT
- Title: NTIRE 2020 Challenge on NonHomogeneous Dehazing
- Authors: Codruta O. Ancuti, Cosmin Ancuti, Florin-Alexandru Vasluianu, Radu
Timofte, Jing Liu, Haiyan Wu, Yuan Xie, Yanyun Qu, Lizhuang Ma, Ziling Huang,
Qili Deng, Ju-Chin Chao, Tsung-Shan Yang, Peng-Wen Chen, Po-Min Hsu, Tzu-Yi
Liao, Chung-En Sun, Pei-Yuan Wu, Jeonghyeok Do, Jongmin Park, Munchurl Kim,
Kareem Metwaly, Xuelu Li, Tiantong Guo, Vishal Monga, Mingzhao Yu,
Venkateswararao Cherukuri, Shiue-Yuan Chuang, Tsung-Nan Lin, David Lee,
Jerome Chang, Zhan-Han Wang, Yu-Bang Chang, Chang-Hong Lin, Yu Dong, Hongyu
Zhou, Xiangzhen Kong, Sourya Dipta Das, Saikat Dutta, Xuan Zhao, Bing Ouyang,
Dennis Estrada, Meiqi Wang, Tianqi Su, Siyi Chen, Bangyong Sun, Vincent
Whannou de Dravo, Zhe Yu, Pratik Narang, Aryan Mehra, Navaneeth Raghunath,
Murari Mandal
- Abstract summary: This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image).
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.
- Score: 127.75499962752747
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of
images (restoration of rich details in hazy image). We focus on the proposed
solutions and their results evaluated on NH-Haze, a novel dataset consisting of
55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.
NH-Haze is the first realistic nonhomogeneous haze dataset that provides ground
truth images. The nonhomogeneous haze has been produced using a professional
haze generator that imitates the real conditions of haze scenes. 168
participants registered in the challenge and 27 teams competed in the final
testing phase. The proposed solutions gauge the state-of-the-art in image
dehazing.
Related papers
- HazeSpace2M: A Dataset for Haze Aware Single Image Dehazing [26.97153700921866]
This research introduces the HazeSpace2M dataset, a collection of over 2 million images designed to enhance dehazing through haze type classification.
Using the dataset, we introduce a technique of haze type classification followed by specialized dehazers to clear hazy images.
Our approach classifies haze types before applying type-specific dehazing, improving clarity in real-life hazy images.
arXiv Detail & Related papers (2024-09-25T23:47:25Z) - NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge [60.21380105535203]
The RAIM challenge constructed a benchmark for image restoration in the wild.
The participants were required to restore the real-captured images from complex and unknown degradation.
Top-ranked methods improved the state-of-the-art restoration performance and obtained unanimous recognition from all 18 judges.
arXiv Detail & Related papers (2024-05-16T09:26:13Z) - Unsupervised Neural Rendering for Image Hazing [31.108654945661705]
Image hazing aims to render a hazy image from a given clean one, which could be applied to a variety of practical applications such as gaming, filming, photographic filtering, and image dehazing.
We propose a neural rendering method for image hazing, dubbed as HazeGEN. To be specific, HazeGEN is a knowledge-driven neural network which estimates the transmission map by leveraging a new prior.
To adaptively learn the airlight, we build a neural module based on another new prior, i.e., the rendered hazy image and the exemplar are similar in the airlight distribution.
arXiv Detail & Related papers (2021-07-14T13:15:14Z) - NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and
Results [181.2861509946241]
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset.
The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark.
arXiv Detail & Related papers (2020-05-08T15:46:19Z) - NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and
Haze-Free Images [95.00583228823446]
NH-HAZE is a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images.
This work presents an objective assessment of several state-of-the-art single image dehazing methods that were evaluated using NH-HAZE dataset.
arXiv Detail & Related papers (2020-05-07T15:50:37Z) - NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and
Results [148.54397669654958]
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable.
In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.
arXiv Detail & Related papers (2020-05-05T08:17:04Z) - 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.