license: mit
task_categories:
- text-classification
language:
- en
tags:
- CVPR
- AI
CVPR 2024 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2024 Open Access website (https://openaccess.thecvf.com/CVPR2024) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2024). For researchers who are interested in doing analysis of CVPR 2024 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2024 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equation Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising",
"authors": "Haijin Zeng, Jiezhang Cao, Kai Zhang, Yongyong Chen, Hiep Luong, Wilfried Philips",
"abstract": "Hyperspectral images (HSIs) have extensive applications in various fields such as medicine agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a challenge due to narrow-band spectral filtering. Consequently the importance of HSI denoising is substantial especially for snapshot hyperspectral imaging technology. While most previous HSI denoising methods are supervised creating supervised training datasets for the diverse scenes hyperspectral cameras and scan parameters is impractical. In this work we present Diff-Unmix a self-supervised denoising method for HSI using diffusion denoising generative models. Specifically Diff-Unmix addresses the challenge of recovering noise-degraded HSI through a fusion of Spectral Unmixing and conditional abundance generation. Firstly it employs a learnable block-based spectral unmixing strategy complemented by a pure transformer-based backbone. Then we introduce a self-supervised generative diffusion network to enhance abundance maps from the spectral unmixing block. This network reconstructs noise-free Unmixing probability distributions effectively mitigating noise-induced degradations within these components. Finally the reconstructed HSI is reconstructed through unmixing reconstruction by blending the diffusion-adjusted abundance map with the spectral endmembers. Experimental results on both simulated and real-world noisy datasets show that Diff-Unmix achieves state-of-the-art performance.",
"pdf": "https://openaccess.thecvf.com/content/CVPR2024/papers/Zeng_Unmixing_Diffusion_for_Self-Supervised_Hyperspectral_Image_Denoising_CVPR_2024_paper.pdf",
"supp": "https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zeng_Unmixing_Diffusion_for_CVPR_2024_supplemental.pdf",
"bibtex": "https://openaccess.thecvf.com",
"url": "https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_Unmixing_Diffusion_for_Self-Supervised_Hyperspectral_Image_Denoising_CVPR_2024_paper.html",
"detail_url": "https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_Unmixing_Diffusion_for_Self-Supervised_Hyperspectral_Image_Denoising_CVPR_2024_paper.html",
"tags": "CVPR 2024"
}
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