Upload Readme and weights

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README.md

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 ---
-license: cc-by-nc-sa-4.0
+license: mit
+datasets:
+  - Aldunitro/HDRT-TIR-diffusion-enhanced
+language:
+  - en
+tags:
+  - thermal
+  - infrared
+  - denoising
+  - diffusion
+  - transformer
+task_categories:
+- image-to-image
+pretty_name: _Noise2DiffusionEnhanced_
+size_categories:
+- 10K<n<100K
 ---
+&nbsp;
+
+<div align="center">
+
+<table>
+<tr>
+
+<td align="center" height="60">
+<a href="https://openaccess.thecvf.com/content/CVPR2026W/PBVS/html/Hazebrouck_Self-supervised_Diffusion-guided_Hallucination-free_Thermal_Infrared_Image_Denoising_CVPRW_2026_paper.html">
+<img src="./README_figures/button_paper.png" height="50"/>
+</a>
+</td>
+
+<td align="center" height="60">
+<a href="https://github.com/HensoldtOptronicsCV/Noise2DiffusionEnhanced">
+<img src="./README_figures/button_github.png" height="50"/>
+</a>
+</td>
+
+<td align="center" height="60">
+<a href="https://huggingface.co/collections/SachyGuy/diffusion-guided-hallucination-free-tir-image-denoising">
+<img src="./README_figures/button_huggingface.png" height="50"/>
+</a>
+</td>
+
+</tr>
+</table>
+
+</div>
+
+&nbsp;
+
+---
+
+# **_Noise2DiffusionEnhanced_ : A Pretrained Ready-to-use TIR Denoising Transformer**
+
+**This is part of the official repository of the paper _Self-supervised Diffusion-guided Hallucination-free Thermal Infrared Image Denoising_, 2026.**  
+&nbsp;
+
+![Graphical Abstract](./README_figures/display_image.png)
+_Our approach uses diffusion-based image enhancement and realistic TIR image degradation to generate image pairs for supervised learning (a) and leverages remarkable visual quality of diffusion models (c) without suffering from hallucinations (d-e)._  
+
+&nbsp;
+## Overview
+
+The _Noise2DiffusionEnhanced_ is part of the work described in "Self-supervised Diffusion-guided Hallucination-free Thermal Infrared Image Denoising", F. Hazebrouck, A. Schock-Schmidtke, N. Stuhrmann,
+J. Fottner, M. Teutsch (2026). The paper is available [here](https://openaccess.thecvf.com/content/CVPR2026W/PBVS/html/Hazebrouck_Self-supervised_Diffusion-guided_Hallucination-free_Thermal_Infrared_Image_Denoising_CVPRW_2026_paper.html).
+
+The _Noise2DiffusionEnhanced_ is a pretrained [Uformer architecture](https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Uformer_A_General_U-Shaped_Transformer_for_Image_Restoration_CVPR_2022_paper.html) [[1]](#1) for TIR sensor noise denoising. It was trained on the [HDRT-TIR-diffusion-enhanced](https://huggingface.co/datasets/Aldunitro/HDRT-TIR-diffusion-enhanced) dataset. The [pretrained weights](./model_best.pth) published here can be inserted in the [GitHub Project](https://github.com/HensoldtOptronicsCV/Noise2DiffusionEnhanced) for inference or fine-tuning.
+
+This pretrained model should contribute to filling the current lack of reference denoising models for Thermal Infrared (TIR) single-image denoising, for direct use as well as for related research.
+
+
+&nbsp;
+## Files
+
+- [model_best.pth](./model_best.pth) is the pretrained-weight file. 
+It can only be used with the associated [project codebase](https://github.com/HensoldtOptronicsCV/Noise2DiffusionEnhanced), where is described how to insert it to run inference or fine-tune the weights.
+
+
+&nbsp;
+## Citation
+
+If you use the _Noise2DiffusionEnhanced_, please cite our work:
+
+    @article{hazebrouck_self-supervised_2026,
+        title = {Self-supervised Diffusion-guided Hallucination-free Thermal Infrared Image Denoising},
+        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+        author = {Hazebrouck, Félix and Schock-Schmidtke, Alexander and Stuhrmann, Norbert and Fottner, Johannes and Teutsch, Michael},
+        year = {2026},
+        pages = {7091--7101},
+    }
+
+
+&nbsp;
+## License and Copyrights  
+The model weights were obtained using different code-compounds, each with its proper license:  
+- The model architecture is a U-Former, introduced in [[1]](#1). The [original code](github.com/ZhendongWang6/Uformer) was adapted for our project and the modified version along with all license-notices with respect to the original source is available on our [project codebase](https://github.com/HensoldtOptronicsCV/Noise2DiffusionEnhanced) and is licensed under the MIT license. As we publish no code parts here, and because the code of [[1]](#1) only indirectly contributed to those weights, we cite them only for transparency.
+- The model was trained with our _HDRT-TIR-DE_ dataset, which is licensed under the [CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0) license. The _HDRT-TIR-DE_ dataset itself is a modified version of the HDRT dataset introduced in [[2]](#2), but because the _HDRT-TIR-DE_ already comply with the licensing of the HDRT, as explicitly stated on the [_HDRT-TIR-DE_ repository](https://huggingface.co/datasets/SachyGuy/HDRT-TIR-DE) and only indirectly contributed to the weights, only the license from the _HDRT-TIR-DE_ dataset applies here.   
+According to the license of the _HDRT-TIR-DE_ Dataset ([CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0)), any "adapted material" must be licensed under the same or a compatible license, therefore including these pre-trained weights.
+- The degradation model used to synthesize the noisy counterparts for the clean images from the _HDRT-TIR-DE_ dataset for the supervised training of the UFormer is adapted from the noise model introduced in [[3]](#3). The [original code](https://github.com/cailijing/MDIVDnet) was adapted for our project and the modified version along with all license-notices with respect to the original source is available on our [project codebase](https://github.com/HensoldtOptronicsCV/Noise2DiffusionEnhanced) and is licensed under the [MIT license](https://opensource.org/license/mit). As we publish no code parts here, and because the code of [[3]](#3) only indirectly contributed to those weights, we cite them only for transparency.
+
+Therefore, the pre-trained weights published in this repository inherit the [CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0) license from the _HDRT-TIR-DE_ dataset. According to this license, we must include the original dataset authors, a copyright notice, a link to original dataset, a link to license, and a statement of modifications.
+
+Authors:  
+© Félix Hazebrouck, Alexander Schock-Schmidtke, Norbert Stuhrmann, Johannes Fottner, Michael Teutsch
+
+Original dataset:  
+_HDRT-TIR-DE_ dataset, available at https://huggingface.co/datasets/SachyGuy/HDRT-TIR-DE, introduced in _Self-supervised Diffusion-guided Hallucination-free Thermal Infrared Image Denoising_ by Hazebrouck et al., 2026.
+
+License:  
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International  
+https://spdx.org/licenses/CC-BY-NC-SA-4.0
+
+Modifications:  
+We encoded the statistical information from the _HDRT-TIR-DE_ dataset in the hereby published pretrained neural-network architecture weights through a process of machine learning.
+
+These pretrained weights are distributed under the same license (CC-BY-NC-SA-4.0).
+
+
+## References
+<a id="1">[1]</a> 
+Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu and Houqiang Li. _Uformer: A general u-shaped transformer for image restoration_. CVPR, 2022
+
+<a id="2">[2]</a> 
+Jingchao Peng, Thomas Bashford-Rogers, Francesco Banterle, Haitao Zhao and Kurt Debattista. _HDRT: A large-scale dataset for infrared-guided HDR imaging_. Elsevier Information Fusion, 120, 2025
+
+<a id="3">[3]</a> 
+Lijing Cai, Xiangyu Dong, Kailai Zhou and Xun Cao. _Exploring video denoising in thermal infrared imaging: Physics-inspired noise generator, dataset, and model_, IEEE 2022

model_best.pth

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