---
license: mit
tags:
- low-light
- low-light-image-enhancement
- image-enhancement
- image-restoration
- computer-vision
- transformer
- transformers
- vision-transformer
- vision-transformers
- image-segmentation
- illumination
- LoRA
- Mixture of Experts
model-index:
- name: ISALux
  results:
  - task:
      type: low-light-image-enhancement
    dataset:
      name: LOL-v1
      type: LOL-v1
    metrics:
    - type: PSNR
      value: 27.63
      name: PSNR
    - type: SSIM
      value: 0.881
      name: SSIM
  - task:
      type: low-light-image-enhancement
    dataset:
      name: LOL-v2-Real
      type: LOL-v2-Real
    metrics:
    - type: PSNR
      value: 29.76
      name: PSNR
    - type: SSIM
      value: 0.908
      name: SSIM
  - task:
      type: low-light-image-enhancement
    dataset:
      name: LOL-v2-Synthetic
      type: LOL-v2-Synthetic
    metrics:
    - type: PSNR
      value: 30.78
      name: PSNR
    - type: SSIM
      value: 0.956
      name: SSIM
  - task:
      type: low-light-image-enhancement
    dataset:
      name: SDSD-indoor
      type: SDSD-indoor
    metrics:
    - type: PSNR
      value: 30.67
      name: PSNR
    - type: SSIM
      value: 0.909
      name: SSIM
  - task:
      type: low-light-image-enhancement
    dataset:
      name: SDSD-outdoor
      type: SDSD-outdoor
    metrics:
    - type: PSNR
      value: 31.58
      name: PSNR
    - type: SSIM
      value: 0.895
      name: SSIM
  - task:
      type: low-light-image-enhancement
    dataset:
      name: LOL Blur
      type: LOL-Blur
    metrics:
    - type: PSNR
      value: 28.01
      name: PSNR
    - type: SSIM
      value: 0.903
      name: SSIM
  - task:
      type: low-light-image-enhancement
    dataset:
      name: MEF
      type: MEF
    metrics:
    - type: NIQE
      value: 3.58
      name: NIQE
  - task:
      type: low-light-image-enhancement
    dataset:
      name: LIME
      type: LIME
    metrics:
    - type: NIQE
      value: 3.91
      name: NIQE
  - task:
      type: low-light-image-enhancement
    dataset:
      name: DICM
      type: DICM
    metrics:
    - type: NIQE
      value: 3.21
      name: NIQE
  - task:
      type: low-light-image-enhancement
    dataset:
      name: NPE
      type: NPE
    metrics:
    - type: NIQE
      value: 3.40
      name: NIQE
pipeline_tag: image-to-image
---

# 🌌 ISALux: Illumination & Semantics Aware Transformer with Mixture of Experts

<div align="center">

👩‍💻 **Authors:**  
[Raul Balmez](https://scholar.google.com/citations?user=vPC7raQAAAAJ&hl=en), [Alexandru Brateanu](https://scholar.google.com/citations?user=ru0meGgAAAAJ&hl=en),  [Ciprian Orhei](https://scholar.google.com/citations?user=DZHdq3wAAAAJ&hl=en), [Codruta Ancuti](https://scholar.google.com/citations?user=5PA43eEAAAAJ&hl=en), [Cosmin Ancuti](https://scholar.google.com/citations?user=zVTgt8IAAAAJ&hl=en)     

📄 [![arXiv](https://img.shields.io/badge/arXiv-2508.17885-b31b1b.svg)](https://arxiv.org/abs/2508.17885)

</div>

---

## 🔎 Abstract
We introduce **ISALux**, a novel transformer-based approach for **Low-Light Image Enhancement (LLIE)** that integrates both illumination and semantic priors.  

✨ Key contributions:
- **HISA-MSA**: A new attention block fusing illumination + semantic segmentation.  
- **Mixture of Experts (MoE)**: Improves contextual learning with conditional activation.  
- **LoRA-enhanced self-attention**: Tackles overfitting across diverse light conditions.  

Extensive experiments on multiple benchmarks demonstrate **state-of-the-art** performance.  
Ablation studies highlight the role of each proposed component.  

---

## 🆕 Updates
- **29.07.2025** 🎉 Our paper [ISALux](https://arxiv.org/abs/2508.17885) is live on arXiv!  
  Dive in to explore methods, results, and ablations. 🚀

---


---

## 📚 Citation

```bibtex
@misc{balmez2025isaluxilluminationsegmentationaware,
  title={ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement}, 
  author={Raul Balmez and Alexandru Brateanu and Ciprian Orhei and Codruta Ancuti and Cosmin Ancuti},
  year={2025},
  eprint={2508.17885},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2508.17885}, 
}