In this paper, we present a comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait. Despite significant advancements in deep learning for image restoration, existing models still encounter substantial challenges in real-world low-light portrait scenarios. Specifically, they struggle to achieve an optimal balance among noise suppression, detail preservation, and faithful illumination and color reproduction. To bridge this gap, this challenge aims to establish a novel benchmark for real-world low-light portrait restoration. We comprehensively evaluate the proposed algorithms utilizing a hybrid evaluation system that integrates objective quantitative metrics with rigorous subjective assessment protocols. For this competition, we provide a dataset containing 800 groups of real-captured low-light portrait data. Each group consists of a 1K-resolution low-light input image, a 1K ground truth (GT), and a 1K person mask. This challenge has garnered widespread attention from both academia and industry, attracting over 100 participating teams and receiving more than 3,000 valid submissions. This report details the motivation behind the challenge, the dataset construction process, the evaluation metrics, and the various phases of the competition. The released dataset and baseline code for this track are publicly available from the same GitHub repository, and the official challenge webpage is hosted on CodaBench.
Figures & tables
Figure 1: Two representative data pairs from the 600 groups of Phase 1 training data. Each pair comprises a 1K-resolution low-light input image, a corresponding 1K-resolution flash portrait ground truth (GT), and a 1K-resolution person mask.
Figure 2: Two representative data pairs from the 100 groups of Phase 2 validation data. Similar to Phase 1, each group includes a 1K low-light input image, a GT, and a person mask. Compared to Phase 1, this split introduces more challenging scenarios, such as distant portraits, to improve the discriminative ability of the evaluation.
Figure 3: Two representative data pairs from the 100 groups of Phase 3 hidden test data. Each group comprises a 1K low-light input, a GT, and a mask, maintaining a scenario distribution and degradation difficulty consistent with the Phase 2 dataset.
Team
LPIPSperson↓
ΔEperson↓
GlobalScore ↑
Phase 2 Score
Rank
nunucccb
0.0266
7.19
0.7843
86.10
2
nanbei
-
-
-
85.21
3
SHL
0.0268
6.83
0.7416
84.91
4
kiritobryant
-
-
-
84.90
5
hezhaokun
0.0270
6.75
0.7388
84.88
6
KC110
0.0284
8.07
0.7647
84.33
7
Table 1: Phase 2 quantitative results on the validation dataset. The “-” symbol indicates that the team either failed to submit valid code during the reproducibility check or their reproduced score deviated significantly from the online leaderboard. ↑ indicates the higher the better, ↓ indicates the lower the better.
Figure 4: Visual comparison of the restoration results from the top 6 teams. The restored images are evaluated against the low-light input and the high-quality GT in terms of facial naturalness, detail preservation, lighting realism, and background cleanliness.
Team
Objective Score
UScore
Final Score
Rank
nunucccb
85.81
89.09
86.794
1
nanbei
84.66
90.00
86.262
2
KC110
85.37
87.27
85.940
3
NJUST-KMG
83.91
88.18
85.191
4
zte_cv
84.28
84.55
84.361
5
NTR
82.94
86.36
83.966
6
Table 2: Final comprehensive results of Phase 3. The Final Score is computed as the weighted sum of the reproduced Objective Score (70%) and the expert blind-test UScore (30%). ↑ indicates the higher the better, ↓ indicates the lower the better.
This paper presents a review of the NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge. The objective of the competition was to merge a set of misaligned smartphone images in the raw domain, captured in low-light conditions, into a single, clean image. Introduced setup simultaneously addresses two problems of low-light photography: visual degradations such as high noise and mixed scene illuminants, and the geometric inconsistencies caused by hand movement during multi-frame capture. To advance research in low-light and nighttime computational photography, a challenging dataset was collected comprising 585 real-world scenes, spanning indoor low-light and outdoor nighttime conditions, for training and benchmarking participant solutions. The competition employed a three-stage evaluation protocol: automatic validation via the CodaBench platform in stages one and two, followed by blind assessment on a private test set for the final ranking. Ten teams surpassed the established baseline, achieving improvements of up to +6.49 dB in PSNR and +0.0101 in SSIM, thereby establishing new state-of-the-art performance for burst-based low-light image enhancement. These results demonstrate significant progress in handling real-world noise, motion, and illumination variability in the low-light setting. Comprehensive results, leaderboards, and additional information are publicly available at https://nightimaging.org.
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.
This paper presents a comprehensive review of the NITRE 2026 Efficient Low Light Image Enhancement (E-LLIE) Challenge, highlighting the proposed solutions and final outcomes. This challenge focuses on mobile image enhancement under low-light conditions, aiming to design lightweight networks that improve enhancement quality while ensuring practical deployability under limited computational resources. A total of 207 participants registered, 27 teams submitted valid entries, and 17 teams ultimately provided valid factsheet. Based on these submissions, this paper provides a systematic evaluation of recent methods for E-LLIE, offering a comprehensive overview of state-of-the-art progress and demonstrating significant improvements in both performance and efficiency.