Update README.md

README.md

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 ---
 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
 ---
 
-Paper: https://arxiv.org/abs/2508.17885
+# 🌌 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}, 
+}