DACG-IR adds a lightweight degradation-aware module that generates prompts to adaptively gate attention temperature, output features, and spatial-channel fusion in an encoder-decoder network for unified image restoration.
citation dossier
arXiv preprint arXiv:1808.04560 (2018)
20Pith papers citing it
21reference links
cs.CVtop field · 20 papers
UNVERDICTEDtop verdict bucket · 17 papers
why this work matters in Pith
Pith has found this work in 20 reviewed papers. Its strongest current cluster is cs.CV (20 papers). The largest review-status bucket among citing papers is UNVERDICTED (17 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
fields
cs.CV 20years
2026 20representative citing papers
A new framework called ERR decomposes UHD image restoration into three frequency stages with specialized sub-networks and introduces the LSUHDIR benchmark dataset of over 82,000 images.
M3D-Stereo supplies 7904 aligned stereo pairs across four multi-degradation scenarios with six progressive levels and pixel-consistent ground truths to benchmark image restoration and stereo matching.
Pre-trained diffusion models inherently support image restoration that can be unlocked by optimizing prompt embeddings at the text encoder output using a diffusion bridge formulation, achieving competitive results on models like WAN and FLUX without fine-tuning.
IQPIR uses NR-IQA-derived quality scores to condition a Transformer and dual-branch codebook for perceptually superior real-world image restoration.
SIMI is an unsupervised low-light image enhancement network using bit-plane decomposition to mine self-information, reported to reach state-of-the-art performance on standard benchmarks.
pHVI-ISPNet achieves state-of-the-art perceptual quality in night photography rendering by combining HVI color space with wavelet feature propagation, sample-adaptive losses, and distribution-based color constancy on the NTIRE 2025 NPR dataset.
Introduces LSTR and ESTR low-light text datasets and shows joint LLIE-OCR training outperforms standalone models.
FDINR decomposes RGB-NIR pairs into frequency components via wavelets and employs dual-branch INR with cross-modal supervision and adaptive uncertainty loss to restore low-light images while enabling arbitrary-resolution output.
RHVI-FDD hierarchically decouples luminance-chrominance and then frequency components in low-light images to correct color, suppress noise, and preserve details better than prior methods.
E-VLA integrates event streams directly into VLA models via lightweight fusion, raising Pick-Place success from 0% to 60-90% at 20 lux and from 0% to 20-25% under severe motion blur.
Stochastic image enhancement methods are shown to be variants of a shared SDE differing in drift, diffusion, terminal distributions and boundary conditions, with controlled experiments revealing no single dominant family and a new modular library released.
SmartPhotoCrafter performs automatic photographic image editing by coupling an Image Critic module that identifies deficiencies with a Photographic Artist module that generates edits, trained via multi-stage pretraining, reasoning supervision, and reinforcement learning.
NAKA-GS combines bionics-inspired Naka chroma correction with progressive point pruning to boost restoration quality and efficiency in low-light 3D Gaussian Splatting.
A deep learning method with an enhanced physical degradation model incorporating anisotropic light spread and hidden skyglow, trained via generative models and synthetic-real coupling, removes light pollution from night cityscape images more effectively than prior restoration techniques.
FLARE-BO uses Bayesian optimization over an eight-parameter space to fuse luminance and adaptive Retinex techniques, reporting marked improvements on the LOL low-light dataset compared to untrained baselines.
ELoG-GS integrates geometry-aware initialization and luminance-guided photometric adaptation into Gaussian Splatting, achieving PSNR 18.66 and SSIM 0.69 on the NTIRE 2026 Track 1 low-light 3D reconstruction benchmark.
A survey that taxonomizes non-Transformer vision models and evaluates their practical trade-offs across efficiency, scalability, and robustness.
NTIRE 2026 challenge report shows progress in low-light image enhancement via 22 submitted networks evaluated on a new dataset.
citing papers explorer
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Degradation-Aware Adaptive Context Gating for Unified Image Restoration
DACG-IR adds a lightweight degradation-aware module that generates prompts to adaptively gate attention temperature, output features, and spatial-channel fusion in an encoder-decoder network for unified image restoration.
-
From Zero to Detail: A Progressive Spectral Decoupling Paradigm for UHD Image Restoration with New Benchmark
A new framework called ERR decomposes UHD image restoration into three frequency stages with specialized sub-networks and introduces the LSUHDIR benchmark dataset of over 82,000 images.
-
M3D-Stereo: A Multiple-Medium and Multiple-Degradation Dataset for Stereo Image Restoration
M3D-Stereo supplies 7904 aligned stereo pairs across four multi-degradation scenarios with six progressive levels and pixel-consistent ground truths to benchmark image restoration and stereo matching.
-
Your Pre-trained Diffusion Model Secretly Knows Restoration
Pre-trained diffusion models inherently support image restoration that can be unlocked by optimizing prompt embeddings at the text encoder output using a diffusion bridge formulation, achieving competitive results on models like WAN and FLUX without fine-tuning.
-
Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration
IQPIR uses NR-IQA-derived quality scores to condition a Transformer and dual-branch codebook for perceptually superior real-world image restoration.
-
SIMI: Self-information Mining Network for Low-light Image Enhancement
SIMI is an unsupervised low-light image enhancement network using bit-plane decomposition to mine self-information, reported to reach state-of-the-art performance on standard benchmarks.
-
Beyond Pixel Fidelity: Minimizing Perceptual Distortion and Color Bias in Night Photography Rendering
pHVI-ISPNet achieves state-of-the-art perceptual quality in night photography rendering by combining HVI color space with wavelet feature propagation, sample-adaptive losses, and distribution-based color constancy on the NTIRE 2025 NPR dataset.
-
Reading in the Dark: Low-light Scene Text Recognition
Introduces LSTR and ESTR low-light text datasets and shows joint LLIE-OCR training outperforms standalone models.
-
Frequency-Decomposed INR for NIR-Assisted Low-Light RGB Image Denoising
FDINR decomposes RGB-NIR pairs into frequency components via wavelets and employs dual-branch INR with cross-modal supervision and adaptive uncertainty loss to restore low-light images while enabling arbitrary-resolution output.
-
RHVI-FDD: A Hierarchical Decoupling Framework for Low-Light Image Enhancement
RHVI-FDD hierarchically decouples luminance-chrominance and then frequency components in low-light images to correct color, suppress noise, and preserve details better than prior methods.
-
E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes
E-VLA integrates event streams directly into VLA models via lightweight fusion, raising Pick-Place success from 0% to 60-90% at 20 lux and from 0% to 20-25% under severe motion blur.
-
Unifying Deep Stochastic Processes for Image Enhancement
Stochastic image enhancement methods are shown to be variants of a shared SDE differing in drift, diffusion, terminal distributions and boundary conditions, with controlled experiments revealing no single dominant family and a new modular library released.
-
SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing
SmartPhotoCrafter performs automatic photographic image editing by coupling an Image Critic module that identifies deficiencies with a Photographic Artist module that generates edits, trained via multi-stage pretraining, reasoning supervision, and reinforcement learning.
-
Naka-GS: A Bionics-inspired Dual-Branch Naka Correction and Progressive Point Pruning for Low-Light 3DGS
NAKA-GS combines bionics-inspired Naka chroma correction with progressive point pruning to boost restoration quality and efficiency in low-light 3D Gaussian Splatting.
-
Deep Light Pollution Removal in Night Cityscape Photographs
A deep learning method with an enhanced physical degradation model incorporating anisotropic light spread and hidden skyglow, trained via generative models and synthetic-real coupling, removes light pollution from night cityscape images more effectively than prior restoration techniques.
-
FLARE-BO: Fused Luminance and Adaptive Retinex Enhancement via Bayesian Optimisation for Low-Light Robotic Vision
FLARE-BO uses Bayesian optimization over an eight-parameter space to fuse luminance and adaptive Retinex techniques, reporting marked improvements on the LOL low-light dataset compared to untrained baselines.
-
ELoG-GS: Dual-Branch Gaussian Splatting with Luminance-Guided Enhancement for Extreme Low-light 3D Reconstruction
ELoG-GS integrates geometry-aware initialization and luminance-guided photometric adaptation into Gaussian Splatting, achieving PSNR 18.66 and SSIM 0.69 on the NTIRE 2026 Track 1 low-light 3D reconstruction benchmark.
-
Attention Is not Everything: Efficient Alternatives for Vision
A survey that taxonomizes non-Transformer vision models and evaluates their practical trade-offs across efficiency, scalability, and robustness.
-
Low Light Image Enhancement Challenge at NTIRE 2026
NTIRE 2026 challenge report shows progress in low-light image enhancement via 22 submitted networks evaluated on a new dataset.
- Leveraging Multimodal Large Language Models for All-in-One Image Restoration via a Mixture of Frequency Experts