RELD: Regularization by Latent Diffusion Models for Image Restoration

• URL: http://arxiv.org/abs/2503.22563v1
• Date: Fri, 28 Mar 2025 16:04:21 GMT
• Title: RELD: Regularization by Latent Diffusion Models for Image Restoration
• Authors: Pasquale Cascarano, Lorenzo Stacchio, Andrea Sebastiani, Alessandro Benfenati, Ulugbek S. Kamilov, Gustavo Marfia,
• Abstract summary: We introduce an approach that integrates a Latent Diffusion Model, trained for the denoising task, into a variational framework using Half-Quadratic Splitting.<n>The proposed strategy, called Regularization by Latent Denoising (RELD), is then tested on a dataset of natural images.
• Score: 41.602636013364574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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