This paper presents NTIRE 2026, the 3rd Restore Any Image Model (RAIM) challenge on multi-exposure image fusion in dynamic scenes. We introduce a benchmark that targets a practical yet difficult HDR imaging setting, where exposure bracketing must be fused under scene motion, illumination variation, and handheld camera jitter. The challenge data contains 100 training sequences with 7 exposure levels and 100 test sequences with 5 exposure levels, reflecting real-world scenarios that frequently cause misalignment and ghosting artefacts. We evaluate submissions with a leaderboard score derived from PSNR, SSIM, and LPIPS, while also considering perceptual quality, efficiency, and reproducibility during the final review. This track attracted 114 participating teams and received 987 submissions. The winning methods significantly improved the ability to remove artifacts from multi-exposure fusion and recover fine details. The dataset and the code of each team can be found at the repository: https://github.com/qulishen/RAIM-HDR.
Figures & tables
Figure 1: Representative image sequences in RAIM MEF in dynamic scenes. The data includes various scenes and times, with a particular focus on scenes with high dynamic range.
Figure 2: Overview of the WHU-VIP method with the AFUNet backbone, the SFT module, and the DCM-RG module.
Figure 3: Overview of the SHL team’s proposed framework. The method introduces a lightweight global guidance branch that provides context from the downsampled full-resolution image, complemented by a Neighboring Pixel Relationships (NPR) loss to preserve high-frequency local details.
Figure 4: Overview of the nunucccb method.
Figure 5: Overview of the untrafusion pipeline.
Team Name
Test Stage 1
Test Stage 2
Final Score ↑
PSNR ↑
SSIM ↑
LPIPS ↓
Score ↑
PSNR ↑
SSIM ↑
LPIPS ↓
Score ↑
WHU-VIP
27.059
0.915
0.089
58.249
27.556
0.923
0.080
59.529
58.889
SHL
27.653
0.920
0.112
57.110
28.825
0.928
0.101
58.958
58.034
nunucccb
26.839
0.913
0.111
56.395
27.011
0.918
0.099
57.605
57.000
untrafusion
26.847
0.908
0.095
57.333
26.002
0.907
0.122
54.733
56.033
I^2 Group & Transsion
26.221
0.904
0.121
54.853
26.137
0.909
0.113
55.619
55.236
Table 1: Verified final results for RAIM Track 2. The ranking is computed from validated scores on Test Stage 1 and Test Stage 2.
Figure 6: Overview of the I2 Group & Transsion method.
Figure 7: TimeDiffiT architecture [ 51 ] . The doubly-corrupted input X~tM and noise level σt enter the time-conditioned U-Net (encoder: orange; decoder: blue), producing the restored output X^ .
Figure 8: Visual comparison on representative examples from Test Stage 1. The compared methods show clear differences in structural consistency, detail preservation, and ghosting suppression.
Figure 9: Visual comparison on representative examples from Test Stage 2. This stage further highlights differences in motion handling, exposure robustness, and artifact suppression.
School of Computer Science and Technology, Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan 430081, China · School of Electronic Information, Wuhan University of Science and Technology, Wuhan 430081, China · Institute of Advanced Intelligence and Computing, A*STAR, Singapore 138632