RELD: Regularization by Latent Diffusion Models for Image Restoration
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912299522981888 |
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| author | Cascarano, Pasquale Stacchio, Lorenzo Sebastiani, Andrea Benfenati, Alessandro Kamilov, Ulugbek S. Marfia, Gustavo |
| author_facet | Cascarano, Pasquale Stacchio, Lorenzo Sebastiani, Andrea Benfenati, Alessandro Kamilov, Ulugbek S. Marfia, Gustavo |
| contents | In recent years, Diffusion Models have become the new state-of-the-art in deep generative modeling, ending the long-time dominance of Generative Adversarial Networks. Inspired by the Regularization by Denoising principle, we introduce an approach that integrates a Latent Diffusion Model, trained for the denoising task, into a variational framework using Half-Quadratic Splitting, exploiting its regularization properties. This approach, under appropriate conditions that can be easily met in various imaging applications, allows for reduced computational cost while achieving high-quality results. The proposed strategy, called Regularization by Latent Denoising (RELD), is then tested on a dataset of natural images, for image denoising, deblurring, and super-resolution tasks. The numerical experiments show that RELD is competitive with other state-of-the-art methods, particularly achieving remarkable results when evaluated using perceptual quality metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RELD: Regularization by Latent Diffusion Models for Image Restoration Cascarano, Pasquale Stacchio, Lorenzo Sebastiani, Andrea Benfenati, Alessandro Kamilov, Ulugbek S. Marfia, Gustavo Image and Video Processing Computer Vision and Pattern Recognition In recent years, Diffusion Models have become the new state-of-the-art in deep generative modeling, ending the long-time dominance of Generative Adversarial Networks. Inspired by the Regularization by Denoising principle, we introduce an approach that integrates a Latent Diffusion Model, trained for the denoising task, into a variational framework using Half-Quadratic Splitting, exploiting its regularization properties. This approach, under appropriate conditions that can be easily met in various imaging applications, allows for reduced computational cost while achieving high-quality results. The proposed strategy, called Regularization by Latent Denoising (RELD), is then tested on a dataset of natural images, for image denoising, deblurring, and super-resolution tasks. The numerical experiments show that RELD is competitive with other state-of-the-art methods, particularly achieving remarkable results when evaluated using perceptual quality metrics. |
| title | RELD: Regularization by Latent Diffusion Models for Image Restoration |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.22563 |