NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods

Jie Cai; Kangning Yang; Zhiyuan Li; Florin-Alexandru Vasluianu; Radu Timofte; Jinlong Li; Jinglin Shen; Zibo Meng

cs.CVApr 11, 2026

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, Wei Zhou, Hongyu Huang, Linfeng Li, Lindong Kong, Saiprasad Meesiyawar, Misbha Falak Khanpagadi, Nikhil Akalwadi, Ramesh Ashok Tabib, Uma Mudenagudi, Bilel Benjdira, Anas M. Ali, Wadii Boulila, Kosuke Shigematsu, Hiroto Shirono, Asuka Shin, Guoyi Xu, Yaoxin Jiang, Jiajia Liu, Yaokun Shi, Jiachen Tu, Shreeniketh Joshi, Jin-Hui Jiang, Yu-Fan Lin, Yu-Jou Hsiao, Chia-Ming Lee, Fu-En Yang, Yu-Chiang Frank Wang, Chih-Chung Hsu

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 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.

Figures & tables

	Methods	Venue	Scheme	Cross-stage fusion
Single-stage	Zhang et al. [ 81 ]	CVPR 2018	$I\rightarrow[T,R]$	N/A
	ERRNet [ 70 ]	CVPR 2019	$I\rightarrow T$	N/A
	RobustSIRR [ 56 ]	CVPR 2023	$I_{multiscale}\rightarrow T$	N/A
	YTMT [ 26 ]	NeurIPS 2021	$I\rightarrow[T,R]$	N/A
	F2T2-HiT [ 6 ]	ICIP 2025	$I\rightarrow T$	N/A
Two-stage	CoRRN [ 64 ]	TPAMI 2019	$I\rightarrow E_{T}$ $[I,E_{T}]\rightarrow T$	Convolutional Fusion

Table 1: I , R , T , and E represent the I nput, R eflection, T ransmission, and E dge map, respectively. The subscripts of T and R represent intermediate process outputs. The Absorption Effect

e

is introduced in [ 84 ] to describe light attenuation as it passes through the glass. The output

residue

term, proposed in [ 27 ] , is used to correct errors in the additive reconstruction of the reflection and transmission layers. Language descriptions in [ 85 ] provide contextual information about the image layers, assisting in addressing the ill-posed nature of the reflection separation problem.

Figure 1: Visualization of paired data generation pipeline for reflection removal.

Figure 2: Visualization of the OPPO AI Reflection Remover pipeline deployed on Find X8 Ultra, using the April 2025 algorithm version. The OPPO reflection removal method adopts a two-stage framework: (1) In the first stage, the system utilizes YOLO and YOSO for robust detection of strong reflection masks, followed by LaMa-based inpainting to fill the detected regions. (2) In the second stage, NAFNet is employed to perform fine-grained reflection removal, further restoring the underlying transmission layer and ensuring high visual fidelity.

Figure 3: Samples of the OpenRR-5k dataset.

Figure 4: The category distribution of our OpenRR-5k test dataset.

Figure 5: Visual comparison on $test_{0004}$ shows that top methods effectively remove reflections while preserving details, whereas lower-ranked methods suffer from residual reflections, over-smoothing, or color distortions, consistent with their lower subjective scores. For instance, while methods from VIP Lab, YuFans, and KLETech-CEVI are capable of suppressing intense reflections, they struggle to faithfully recover the underlying background details.

Figure 6: Visual comparison across diverse OpenRR-5k test real-world scenes. Top-ranked methods (RRay, Xreflect Master, and AIIALab) consistently achieve superior reflection removal while preserving fine details and faithful colors. In contrast, lower-ranked methods often exhibit residual reflections, over-smoothing, or color distortions, which aligns with their lower subjective rankings. The ten rows from top to bottom correspond to test samples $test_{0005}$ , $test_{0006}$ , $test_{0023}$ , $test_{0026}$ , $test_{0029}$ , $test_{0049}$ , $test_{0075}$ , $test_{0077}$ , $test_{0096}$ , and $test_{0099}$ , respectively.

	Validation Set					Test Set
Team Name	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	NIQE $\downarrow$	Subjective Score $\uparrow$
RRay	36.1688 (1)	0.9758 (3)	0.0235 (4)	0.0135 (2)	3.7375 (2)	4.45 (1)
Xreflect Master	36.0496 (2)	0.9776 (1)	0.0210 (1)	0.0127 (1)	3.7648 (6)	4.31 (2)
AIIALab	35.3799 (3)	0.9750 (4)	0.0231 (2)	0.0155 (5)	3.7737 (9)	4.23 (3)
VIP Lab	34.6872 (5)	0.9766 (2)	0.0231 (2)	0.0148 (3)	3.7218 (1)	3.85 (4)
YuFans	34.9062 (4)	0.9738 (8)	0.0257 (6)	0.0159 (6)	3.7783 (10)	3.59 (5)

Table 2: Objective and subjective results on the NTIRE 2026 SIRR Challenge. Note: objective metrics are computed on the OpenRR-5k val ; subjective scores are calculated on the OpenRR-5k test . NTIRE 2026 SIRR in the Wild Challenge Award Winners: 1st Prize: RRay; 2nd Prizes: Xreflect Master, AIIALab; 3rd Prizes: VIP Lab, YuFans, KLETech-CEVI.

Figure 7: An overview of RdNafNet for SIRR.

Figure 8: Overview of the proposed RDNet-XL framework with diffusion-based distillation.

Figure 9: An overview of MS-RDNet for SIRR. Code Link: https://github.com/BrewieRain/MS-RDNet

Figure 10: The overall architecture of the proposed network. Code Link: github.com/j0nghyukpark/CompSIRS

Panel of Figure 11 — Figure 11: Overview of our approach. Top: Two-stage fine-tuning pipeline with SWA and geometric TTA. Numbers on arrows indicate validation PSNR at each stage. Bottom: RDNet architecture with FocalNet-Large backbone, RevCol body, and three separate NAFBlock decoders. Code Link: Google Drive

Figure 12: Overview of the proposed reflection removal framework based on the XReflection architecture. The network learns to recover the clean transmission layer from a reflection-contaminated input image through hierarchical feature extraction and residual reflection estimation. Code Link: https://github.com/CeviKle/NTIRE2026-KLETech-CEVI-SIRR.git

Figure 13: DUSKAN architecture. Symmetric 4-level U-Net with DUSKANBlock stages. Path A (blue) extracts global features via FFT magnitude modulation, enriched with spectral positional encoding and SE reweighting. Path B (red) uses Kolmogorov-Arnold polynomial-basis activations with a parallel-additive selective gate. A learned per-stage logit $\alpha$ blends both outputs. Code Link: Google Drive

Figure 14: Overview of the final SiGMoid pipeline used for the NTIRE 2026 SIRR final submission. Code Link: Google Drive

Figure 15: TimeDiffiT architecture. The doubly-corrupted input $\tilde{X}_{t}^{M}$ and noise level $\sigma_{t}$ enter the time-conditioned U-Net (encoder: orange; decoder: blue), producing the restored output $\hat{X}$ . During SFT, no masking is applied and $\sigma_{t}=0$ .

Figure 16: Overview of our proposed SIRR-Net for single image reflection removal. The U-Net encoder-decoder processes the blended input through four resolution levels, with each stage comprising stacked Transformer Blocks (MDTA + GDFN + SE). Skip connections fuse encoder and decoder features. A global residual connection adds the input to the output. Code Link: https://github.com/Shreenikethjoshi/NTIRE2026-Reflection-removal-in-the-wild

Figure 17: An overview of RDNet+ for SIRR. Code Link: https://github.com/wuw2135/RDNet-with-Frozen-DINOv2-Semantic-Prior-for-Reflection-Removal

Figure 18: Visual comparison of reflection removal results. From left to right: input images, and results generated by OPPO-baseline, RRay, gpt-image-1.5, gpt-image-2, Nano Banana Pro, and Nano Banana 2. The generative methods (gpt-image series and Nano Banana series) demonstrate superior performance in restoring background details and suppressing artifacts. However, as seen in the second row, generative models may occasionally misidentify intense reflections as background structures, leading to unintended artifact amplification.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 19: Visual comparison on $test_{0074}$ .

Figure 20: Visual comparison on $test_{0011}$ .

Figure 21: Visual comparison on $test_{0015}$ .

Figure 22: Visual comparison on $test_{0044}$ .

Figure 23: Visual comparison on $test_{0069}$ .

Figure 24: Visual comparison on $test_{0084}$ .

Figure 25: Visual comparison on $test_{0090}$ .

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GFRRN: Explore the Gaps in Single Image Reflection Removal

Yu Chen, Zewei He, Xingyu Liu, Zixuan Chen+1

Zhejiang University · Huanjiang Laboratory · The Chinese University of Hong Kong

Single-Image Reflection Removal Image Restoration

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Prior dual-stream methods with the feature interaction mechanism have achieved remarkable performance in single image reflection removal (SIRR). However, they often struggle with (1) semantic understanding gap between the features of pre-trained models and those of reflection removal models, and (2) reflection label inconsistencies between synthetic and real-world training data. In this work, we first adopt the parameter efficient fine-tuning (PEFT) strategy by integrating several learnable Mona layers into the pre-trained model to align the training directions. Then, a label generator is designed to unify the reflection labels for both synthetic and real-world data. In addition, a Gaussian-based Adaptive Frequency Learning Block (G-AFLB) is proposed to adaptively learn and fuse the frequency priors, and a Dynamic Agent Attention (DAA) is employed as an alternative to window-based attention by dynamically modeling the significance levels across windows (inter-) and within an individual window (intra-). These components constitute our proposed Gap-Free Reflection Removal Network (GFRRN). Extensive experiments demonstrate the effectiveness of our GFRRN, achieving superior performance against state-of-the-art SIRR methods.

Abstract

Single-Image Reflection Removal

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