73
39 commits
updated Jul 1, 2026
Awesome Multimodal Large Language Models In Low-level Vision
🔥A curated list of awesome Multimodal Large Language Models(MLLMs) & Vision-Language Models(VLMs) in low-level vision.🔥
Please feel free to offer your suggestions in the Issues and pull requests to add links.
[ Last updated at 2026/07/01 ]
Diffusion Models in Low-Level Vision: A Survey
Chunming He, Yuqi Shen, Chengyu Fang, Fengyang Xiao, Longxiang Tang, Yulun Zhang, Wangmeng Zuo, Zhenhua Guo, Xiu Li
TPAMI. [Paper]
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
Chunming He, Chengyu Fang, Yulun Zhang, Kai Li, Longxiang Tang, Chenyu You, Fengyang Xiao, Zhenhua Guo, Xiu Li
ICLR 2025, Spotlight. [Paper] [Github]
Jan. 2025
| Title | Venue | Date | Code |
|---|---|---|---|
Q-Instruct: Improving Low-Level Visual Abilities for Multi-Modality Foundation Models | CVPR | 2023-11 | Github |
| Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision | arXiv | 2025-04 | - |
| Title | Venue | Date | Code |
|---|---|---|---|
Grounding Language Models to Images for Multimodal Inputs and Outputs :contentReference[oaicite:0]{index=0} | ICML | 2023-01 | Github |
Prismer: A Vision-Language Model with Multi-Task Experts | TMLR | 2023-03 | Github |
LM4LV: A Frozen Large Language Model for Low-level Vision Tasks | arXiv | 2024-05 | Github |
Frozen Transformers in Language Models Are Effective Visual Encoder Layers | ICLR | 2023-10 | Github |
| Task | Dataset | Size | Repo / Download | Description |
|---|---|---|---|---|
| SR | DIV2K | 900/100 | Download | High-resolution natural images for super-resolution with synthetic degradations. |
| SR | RealSR | 595 | Repo | Real-captured paired low-resolution and high-resolution images using focal-length changes. |
| SR | DRealSR | 2,507 | Repo | Real-world super-resolution pairs collected across indoor and outdoor scenes. |
| LLIE | LOLv1 | 585/15 | Download | Paired low-light and normal-light real images for low-light enhancement. |
| LLIE | MIT-Adobe FiveK | 25,000 | Download | Expert-retouched photo pairs for illumination and tone related enhancement tasks. |
| LLIE | LOLv2 | 1,589/200 | Repo | Combined real and synthetic paired data for low-light enhancement. |
| Dehaze | RESIDE | 13,000/990 | Repo | Dehazing benchmark including synthetic and real-world subsets. |
| Dehaze | NH-Haze | 55 | Download | Real paired outdoor images with non-homogeneous haze. |
| Dehaze | Haze-4K | 4,000 | Repo | Synthetic 4K dehazing pairs with auxiliary physical annotations. |
| Inpainting | CelebA | 200,000 | Download | Large-scale face dataset used for face-centric restoration and inpainting. |
| Inpainting | CelebA-HQ | 30,000 | Repo | High-quality face images derived from CelebA for high-fidelity restoration. |
| Inpainting, Derain | MSCOCO | 328,124 | Download | Large-scale natural images with segmentation annotations for generic restoration and editing. |
| Derain | Rain100H | 1,800/100 | Download | Synthetic heavy-rain streak pairs for supervised deraining. |
| Derain | RainDrop | 861/239 | Repo | Real paired images with adherent raindrops for deraining and restoration. |
| Deblur | GoPro | 2,103/1,111 | Download | Motion blur benchmark synthesized from high-frame-rate video frames. |
| Deblur | RealBlur | 3,758/980 | Repo | Real paired short-exposure and long-exposure images for motion deblurring. |
| Deblur | HIDE | 8,422 | Repo | Human-centered motion deblurring dataset with complex dynamic motion. |
| CT denoising | 2016 NIH-AAPM-Mayo | 5,936 | Download | Low-dose CT dataset used for dose reduction and denoising research. |
| CT denoising | Mayo-2016 | 4,800/1,136 | Download | Challenge dataset used for low-dose CT denoising and reconstruction. |
| CT denoising | Mayo-2020 | 2,400/580 | Download | Preprocessed low-dose CT dataset for training and evaluation. |
| Title | Venue | Date | Code | Note |
|---|---|---|---|---|
Diffusion Models in Low-Level Vision: A Survey | TPAMI | 2024-06 | Repo | Diffusion-based methods for low-level vision, including task formulations, architectures, and training design patterns. |
A Survey on All-in-One Image Restoration: Taxonomy, Methods, and Future Directions | arXiv | 2024-10 | Repo | All-in-one image restoration models covering multi-task, multi-degradation, unified training, and evaluation protocols. |
Instruction Guided Editing Controls for Images and Multimedia: A Survey in the Era of Large Language Models | arXiv | 2024-11 | Repo | Instruction-driven image and multimedia editing, covering control types, model families, and evaluation for edit fidelity and consistency. |
Vision-Language Models for Vision Tasks: A Survey | TPAMI | 2023-04 | Repo | VLM methodology for vision tasks, including pretraining, transfer learning, distillation, datasets, and benchmarks. |
A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges | CVPRW | 2025-01 | Repo | LVLM design and evaluation, focusing on alignment, benchmark suites, failure modes, and measurement practices. |
A Survey on Multimodal Large Language Models | NSR | 2023-06 | Repo | Core MLLM architectures, training data, instruction tuning, evaluation, and common issues such as hallucination. |
| Vision Language Models: A Survey of 26K Papers | arXiv | 2025-10 | - | Trend analysis and taxonomy at scale, summarizing research directions from a large paper collection. |
| Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions | arXiv | 2024-02 | - | Broad overview of VLM model families, training paradigms, and benchmarks, with an emphasis on capabilities and limitations. |
| Vision Encoders in Vision-Language Models: A Survey | Tech Report | 2025-12 | - | Vision encoder design choices for VLMs, including architecture families, scaling trends, and practical tradeoffs. |
73
39 commits
updated Jul 1, 2026
Awesome Multimodal Large Language Models In Low-level Vision
🔥A curated list of awesome Multimodal Large Language Models(MLLMs) & Vision-Language Models(VLMs) in low-level vision.🔥
Please feel free to offer your suggestions in the Issues and pull requests to add links.
[ Last updated at 2026/07/01 ]
Diffusion Models in Low-Level Vision: A Survey
Chunming He, Yuqi Shen, Chengyu Fang, Fengyang Xiao, Longxiang Tang, Yulun Zhang, Wangmeng Zuo, Zhenhua Guo, Xiu Li
TPAMI. [Paper]
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
Chunming He, Chengyu Fang, Yulun Zhang, Kai Li, Longxiang Tang, Chenyu You, Fengyang Xiao, Zhenhua Guo, Xiu Li
ICLR 2025, Spotlight. [Paper] [Github]
Jan. 2025
| Title | Venue | Date | Code |
|---|---|---|---|
Q-Instruct: Improving Low-Level Visual Abilities for Multi-Modality Foundation Models | CVPR | 2023-11 | Github |
| Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision | arXiv | 2025-04 | - |
| Title | Venue | Date | Code |
|---|---|---|---|
Grounding Language Models to Images for Multimodal Inputs and Outputs :contentReference[oaicite:0]{index=0} | ICML | 2023-01 | Github |
Prismer: A Vision-Language Model with Multi-Task Experts | TMLR | 2023-03 | Github |
LM4LV: A Frozen Large Language Model for Low-level Vision Tasks | arXiv | 2024-05 | Github |
Frozen Transformers in Language Models Are Effective Visual Encoder Layers | ICLR | 2023-10 | Github |
| Task | Dataset | Size | Repo / Download | Description |
|---|---|---|---|---|
| SR | DIV2K | 900/100 | Download | High-resolution natural images for super-resolution with synthetic degradations. |
| SR | RealSR | 595 | Repo | Real-captured paired low-resolution and high-resolution images using focal-length changes. |
| SR | DRealSR | 2,507 | Repo | Real-world super-resolution pairs collected across indoor and outdoor scenes. |
| LLIE | LOLv1 | 585/15 | Download | Paired low-light and normal-light real images for low-light enhancement. |
| LLIE | MIT-Adobe FiveK | 25,000 | Download | Expert-retouched photo pairs for illumination and tone related enhancement tasks. |
| LLIE | LOLv2 | 1,589/200 | Repo | Combined real and synthetic paired data for low-light enhancement. |
| Dehaze | RESIDE | 13,000/990 | Repo | Dehazing benchmark including synthetic and real-world subsets. |
| Dehaze | NH-Haze | 55 | Download | Real paired outdoor images with non-homogeneous haze. |
| Dehaze | Haze-4K | 4,000 | Repo | Synthetic 4K dehazing pairs with auxiliary physical annotations. |
| Inpainting | CelebA | 200,000 | Download | Large-scale face dataset used for face-centric restoration and inpainting. |
| Inpainting | CelebA-HQ | 30,000 | Repo | High-quality face images derived from CelebA for high-fidelity restoration. |
| Inpainting, Derain | MSCOCO | 328,124 | Download | Large-scale natural images with segmentation annotations for generic restoration and editing. |
| Derain | Rain100H | 1,800/100 | Download | Synthetic heavy-rain streak pairs for supervised deraining. |
| Derain | RainDrop | 861/239 | Repo | Real paired images with adherent raindrops for deraining and restoration. |
| Deblur | GoPro | 2,103/1,111 | Download | Motion blur benchmark synthesized from high-frame-rate video frames. |
| Deblur | RealBlur | 3,758/980 | Repo | Real paired short-exposure and long-exposure images for motion deblurring. |
| Deblur | HIDE | 8,422 | Repo | Human-centered motion deblurring dataset with complex dynamic motion. |
| CT denoising | 2016 NIH-AAPM-Mayo | 5,936 | Download | Low-dose CT dataset used for dose reduction and denoising research. |
| CT denoising | Mayo-2016 | 4,800/1,136 | Download | Challenge dataset used for low-dose CT denoising and reconstruction. |
| CT denoising | Mayo-2020 | 2,400/580 | Download | Preprocessed low-dose CT dataset for training and evaluation. |
| Title | Venue | Date | Code | Note |
|---|---|---|---|---|
Diffusion Models in Low-Level Vision: A Survey | TPAMI | 2024-06 | Repo | Diffusion-based methods for low-level vision, including task formulations, architectures, and training design patterns. |
A Survey on All-in-One Image Restoration: Taxonomy, Methods, and Future Directions | arXiv | 2024-10 | Repo | All-in-one image restoration models covering multi-task, multi-degradation, unified training, and evaluation protocols. |
Instruction Guided Editing Controls for Images and Multimedia: A Survey in the Era of Large Language Models | arXiv | 2024-11 | Repo | Instruction-driven image and multimedia editing, covering control types, model families, and evaluation for edit fidelity and consistency. |
Vision-Language Models for Vision Tasks: A Survey | TPAMI | 2023-04 | Repo | VLM methodology for vision tasks, including pretraining, transfer learning, distillation, datasets, and benchmarks. |
A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges | CVPRW | 2025-01 | Repo | LVLM design and evaluation, focusing on alignment, benchmark suites, failure modes, and measurement practices. |
A Survey on Multimodal Large Language Models | NSR | 2023-06 | Repo | Core MLLM architectures, training data, instruction tuning, evaluation, and common issues such as hallucination. |
| Vision Language Models: A Survey of 26K Papers | arXiv | 2025-10 | - | Trend analysis and taxonomy at scale, summarizing research directions from a large paper collection. |
| Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions | arXiv | 2024-02 | - | Broad overview of VLM model families, training paradigms, and benchmarks, with an emphasis on capabilities and limitations. |
| Vision Encoders in Vision-Language Models: A Survey | Tech Report | 2025-12 | - | Vision encoder design choices for VLMs, including architecture families, scaling trends, and practical tradeoffs. |