References for existing research papers:
1. Big picture
2. Low‑light image enhancement (general)
2.1 Retinex‑based Transformers
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement – ICCV 2023
- Key idea: Formulates a One‑stage Retinex‑based Framework (ORF): first estimates illumination to “light‑up” the image, then restores corruptions (noise, artifacts) with an Illumination‑Guided Transformer (IGT) that uses illumination representations to guide non‑local interactions across regions with different lighting.
- Status: First Transformer‑based method for LLIE; significantly outperforms prior SOTA on 13 benchmarks and shows gains in low‑light object detection. Code/models widely used as baseline; second place in NTIRE 2024 Low Light Enhancement Challenge.
LightingFormer: Transformer-CNN hybrid network for low-light image enhancement – Computers & Graphics 2024
- Key idea: Proposes a Transformer–CNN hybrid block with mixed attention: Transformer models long‑range dependencies, CNN extracts low‑level local features; uses a U‑Net discriminator for adaptive region‑wise enhancement and to suppress over/underexposure and noise.
- Status: Reported to outperform prior SOTA quantitatively and qualitatively, with improved downstream object detection.
2.2 SNR‑aware models
SNR-Aware Low-Light Image Enhancement – CVPR 2022
- Key idea: Introduces an SNR‑aware Transformer that uses a signal‑to‑noise ratio prior map to guide feature fusion and avoid tokens from very low‑SNR regions; combines with CNN for spatially varying enhancement.
- Status: Consistently better than prior SOTA on 7 benchmarks; validated by large‑scale user study.
Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer – 2023
- Key idea: Dual‑branch Swin Transformer network guided by an SNR prior map; uses unsupervised Retinex‑based losses so training does not require paired data.
- Status: Competitive with baselines while reducing reliance on paired supervision.
SMT: SNR-Aware Mamba-Transformer for Low-Light Image Enhancement – PRCV 2025
- Key idea: Introduces an SNR‑aware Mamba module for global noise modeling, an eight‑directional Mamba scanning mechanism for multi‑perspective context, and a multi‑scale attention aggregation module for fine detail recovery.
- Status: Outperforms or matches SOTA on 8 benchmarks with better visual quality.
2.3 Efficient / self‑calibrated illumination learning
Toward Fast, Flexible, and Robust Low-Light Image Enhancement (SCI / SCI++) – CVPR 2022
- Key idea: Self‑Calibrated Illumination learning: cascaded illumination stages with weight sharing, plus a self‑calibrator so only a single basic block is used at inference. SCI++ further disentangles self‑calibrator and illumination estimator for better convergence and interpretability.
- Status: Very strong trade‑off between quality and speed; unsupervised loss improves generalization to in‑the‑wild scenes.
LLFormer: Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method – AAAI 2023
- Key idea: Proposes a 4K/8K UHD‑LOL benchmark and LLFormer, whose core components are axis‑based multi‑head self‑attention (height/width axes sequentially) and cross‑layer attention fusion to keep complexity linear.
- Status: Outperforms prior SOTA on UHD‑LOL and standard LOL datasets; widely used as UHD LLIE baseline.
2.4 Color space / decoupling strategies
HVI: A New Color Space for Low-Light Image Enhancement / HVI-CIDNet+ – 2025
- Key idea: Proposes Horizontal/Vertical‑Intensity (HVI) color space designed to reduce red/black artifacts common in RGB and HSV; introduces Color and Intensity Decoupling Network (CIDNet) with a Lighten Cross‑Attention (LCA) block to learn accurate photometric mapping in HVI space.
- Status: Outperforms SOTA on 10 datasets; HVI‑CIDNet is core of 1st place solution in NTIRE 2025 Low Light Image Enhancement Challenge.
Low-light Image Enhancement Algorithm Based on Transformer – SPIC 2024
- Key idea: Redesigns LLIE network using Transformer: extracts channel‑wise self‑attention features, fuses with shallow features, then refines; emphasizes efficiency and global dependencies vs traditional Retinex/CNN pipelines.
- Status: Representative 2024 Transformer LLIE work with improved performance and efficiency over prior CNN‑based methods.
CF-UFormer: Low-light image enhancement using transformer with color fusion – 2024
- Key idea: U‑shape Transformer with color fusion module to better handle color and luminance jointly; improves visual quality and stability under severe low‑light conditions.
2.5 Surveys & benchmarks (LLIE)
- A Survey of Deep Learning-Based Low-Light Image Enhancement – Sensors 2023
Systematic classification of DL‑based LLIE methods, datasets, and metrics; summarizes representative CNN/GAN/Transformer models and their trade‑offs. - A survey on image enhancement for Low-light images – 2023
Focuses on DL‑based vs traditional methods and data characteristics. - NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results – CVPRW 2024
Challenge overview; top solutions set practical SOTA on standard LOL and real‑world datasets. - NTIRE 2026 Challenge on Efficient Low Light Image Enhancement (E‑LLIE) – 2026
Overview of efficient LLIE methods; emphasizes lightweight, mobile‑friendly architectures.
3. Shadow removal
3.1 Global context Transformers
ShadowFormer: Global Context Helps Shadow Removal – AAAI 2023
- Key idea: Derives a Retinex‑based shadow model and proposes Shadow‑Interaction Module (SIM) with Shadow‑Interaction Attention (SIA) to model context correlation between shadow and non‑shadow regions in a multi‑scale channel attention Transformer.
- Status: Achieves SOTA on ISTD, ISTD+, SRD with up to 150× fewer parameters than prior methods.
CRFormer: A cross-region transformer for shadow removal – 2024
- Key idea: Introduces region‑aware cross‑attention to transfer features from non‑shadow to shadow regions, improving boundary consistency and reducing artifacts.
- Status: Strong performer on standard shadow datasets; cross‑region design is influential.
3.2 Mask‑free / FFT‑based methods
ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer – CVPRW 2024
- Key idea: Mask‑free shadow removal and refinement via Fast Fourier Transformer; learns mappings in both spatial and frequency domains using Fast‑Fourier Attention (FFAT) to reduce pixel misalignment and refine details.
- Status: 1st place in NTIRE 2024 Shadow Removal Perceptual Track, 2nd in Fidelity Track.
3.3 Diffusion & hybrid models
ShadowFormer++: multi-scale shadow priors and diffusion refinement – 2025
- Key idea: Combines Transformer + diffusion: Multi‑Scale Local Shadow Perception (MS‑LSPM), Shadow‑Aware Transformer Encoder (SATE), and Diffusion‑Inspired Refinement Module (DIRM) for progressive shadow‑free reconstruction.
- Status: Achieves SOTA MAE/PSNR/SSIM on ISTD/ISTD+/SRD.
3.4 Surveys & benchmarks (shadows)
- Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey – 2024
Detailed taxonomy of shadow removal networks, losses, and datasets; summarizes quantitative/qualitative comparisons. - Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation – IJCV 2026
Unified survey and benchmark for shadow detection/removal/generation; re‑trains representative methods under standardized settings and highlights cross‑dataset generalization issues. - Awesome-Shadow-Removal – curated list of supervised/unsupervised deep shadow methods.
4. Specular / highlight removal
4.1 Attention‑based highlight removal
Dual-Hybrid Attention Network for Specular Highlight Removal (DHAN-SHR) – ACM Multimedia 2024
- Key idea: End‑to‑end network with Adaptive Local Hybrid‑Domain Dual Attention Transformer (L‑HD‑DAT) and Adaptive Global Dual Attention Transformer (G‑DAT); models inter‑channel/pixel and spectral‑domain dependencies without extra priors.
- Status: Outperforms 18 SOTA methods on a large‑scale benchmark, setting a new standard for specular highlight removal.
4.2 Coarse‑to‑fine + diffusion refinement
Document Specular Highlight Removal with Coarse-to-Fine Strategy – ICDAR 2024
- Key idea: Three‑module framework:
- Coarse Predictor (CP): Transformer‑based U‑Net recovers main content.
- Global Discriminator (GD): Ensures global consistency of coarse result.
- Refinement Predictor (RP): Conditional Denoising Diffusion Probabilistic Model (DDPM) predicts residual between GT and CP output.
- Status: Surpasses SOTA on four highlight benchmarks.
4.3 Privacy‑preserving / federated learning
Specular highlight removal by federated generative adversarial network with attention mechanism (FL-AttGAN) – Scientific Reports 2024
- Key idea: Uses federated learning to train a global highlight‑removal model without centralizing sensitive images; combines AttGAN with attention mechanisms to focus on highlight regions and improve realism.
- Status: Outperforms existing methods on SD1/SD2/RD datasets under privacy‑preserving constraints.
