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The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
Authors:
Kai Liu,
Haoyang Yue,
Zeli Lin,
Zheng Chen,
Jingkai Wang,
Jue Gong,
Jiatong Li,
Xianglong Yan,
Libo Zhu,
Jianze Li,
Ziqing Zhang,
Zihan Zhou,
Xiaoyang Liu,
Radu Timofte,
Yulun Zhang,
Junye Chen,
Zhenming Yan,
Yucong Hong,
Ruize Han,
Song Wang,
Li Pang,
Heng Zhao,
Xinqiao Wu,
Deyu Meng,
Xiangyong Cao
, et al. (43 additional authors not shown)
Abstract:
This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high-resolution (HR) infrared images from low-resolution (LR) inputs generated through bicubic downsampling with a x4 scaling factor. The objective is to develop effective models or solutions that achieve state-of-the-art pe…
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This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high-resolution (HR) infrared images from low-resolution (LR) inputs generated through bicubic downsampling with a x4 scaling factor. The objective is to develop effective models or solutions that achieve state-of-the-art performance for infrared image SR in remote sensing scenarios. To reflect the characteristics of infrared data and practical application needs, the challenge adopts a single-track setting. A total of 115 participants registered for the competition, with 13 teams submitting valid entries. This report summarizes the challenge design, dataset, evaluation protocol, main results, and the representative methods of each team. The challenge serves as a benchmark to advance research in infrared image super-resolution and promote the development of effective solutions for real-world remote sensing applications.
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Submitted 23 April, 2026;
originally announced April 2026.
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Low Light Image Enhancement Challenge at NTIRE 2026
Authors:
George Ciubotariu,
Sharif S M A,
Abdur Rehman,
Fayaz Ali Dharejo,
Rizwan Ali Naqvi,
Marcos V. Conde,
Radu Timofte,
Zhi Jin,
Hongjun Wu,
Wenjian Zhang,
Chang Ye,
Xunpeng Yi,
Qinglong Yan,
Yibing Zhang,
Zaynab Ali,
Saiprasad Meesiyawar,
Varda I Pattanshetty,
Varsha I Pattanshetty,
Nikhil Akalwadi,
Padmashree Desai,
Ramesh Ashok Tabib,
Uma Mudenagudi,
Hao Yang,
Ruikun Zhang,
Liyuan Pan
, et al. (68 additional authors not shown)
Abstract:
This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions by learning representative visual cues with the purpose of restoring information…
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This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions by learning representative visual cues with the purpose of restoring information loss due to low-contrast and noisy images. A total of 195 participants registered for the first track and 153 for the second track of the competition, and 22 teams ultimately submitted valid entries. This paper thoroughly evaluates the state-of-the-art advances in (joint denoising and) low-light image enhancement, showcasing the significant progress in the field, while leveraging samples of our novel dataset.
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Submitted 14 May, 2026; v1 submitted 19 April, 2026;
originally announced April 2026.
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The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Authors:
Xingyu Qiu,
Yuqian Fu,
Jiawei Geng,
Bin Ren,
Jiancheng Pan,
Zongwei Wu,
Hao Tang,
Yanwei Fu,
Radu Timofte,
Nicu Sebe,
Mohamed Elhoseiny,
Lingyi Hong,
Mingxi Cheng,
Xingqi He,
Runze Li,
Xingdong Sheng,
Wenqiang Zhang,
Jiacong Liu,
Shu Luo,
Yikai Qin,
Yaze Zhao,
Yongwei Jiang,
Yixiong Zou,
Zhe Zhang,
Yang Yang
, et al. (49 additional authors not shown)
Abstract:
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across distinct domains. As part of NTIRE 2026, we hosted the second CD-FSOD Challenge to systematically evaluate and promote progress in detecting objects in unseen target domains under limited annotation conditions. The chal…
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Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across distinct domains. As part of NTIRE 2026, we hosted the second CD-FSOD Challenge to systematically evaluate and promote progress in detecting objects in unseen target domains under limited annotation conditions. The challenge received strong community interest, with 128 registered participants and a total of 696 submissions. Among them, 31 teams actively participated, and 19 teams submitted valid final results. Participants explored a wide range of strategies, introducing innovative methods that push the performance frontier under both open-source and closed-source tracks. This report presents a detailed overview of the NTIRE 2026 CD-FSOD Challenge, including a summary of the submitted approaches and an analysis of the final results across all participating teams. Challenge Codes: https://github.com/ohMargin/NTIRE2026_CDFSOD.
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Submitted 13 April, 2026;
originally announced April 2026.
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NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods
Authors:
Jie Cai,
Kangning Yang,
Zhiyuan Li,
Florin-Alexandru Vasluianu,
Radu Timofte,
Jinlong Li,
Jinglin Shen,
Zibo Meng,
Junyan Cao,
Lu Zhao,
Pengwei Liu,
Yuyi Zhang,
Fengjun Guo,
Jiagao Hu,
Zepeng Wang,
Fei Wang,
Daiguo Zhou,
Yi'ang Chen,
Honghui Zhu,
Mengru Yang,
Yan Luo,
Kui Jiang,
Jin Guo,
Jonghyuk Park,
Jae-Young Sim
, et al. (28 additional authors not shown)
Abstract:
In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academic research, most methods are tested on synthetic images or limited real-world images, creating a gap in real-world applications. In this challenge, we provide participants with the OpenRR-5k dataset. This dataset requir…
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In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academic research, most methods are tested on synthetic images or limited real-world images, creating a gap in real-world applications. In this challenge, we provide participants with the OpenRR-5k dataset. This dataset requires participants to process real-world images covering a range of reflection scenarios and intensities, aiming to generate clean images without reflections. The challenge attracted more than 100 registrations, with eleven of them participating in the final testing phase. The top-ranked methods advanced the state-of-the-art reflection removal performance and earned unanimous recognition from five experts in the field. The proposed OpenRR-5k dataset is available at https://huggingface.co/datasets/qiuzhangTiTi/OpenRR-5k, and the homepage of this challenge is at https://github.com/caijie0620/OpenRR-5k.
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Submitted 4 August, 2026; v1 submitted 11 April, 2026;
originally announced April 2026.