arXiv Computer Vision
Aug 25

Targeted Iterative Filtering

The paper introduces a novel nonlinear diffusion scheme for image denoising, derived from a linear diffusion process applied in a value space tailored to specific application domains such as image compression, still-image acquisition, and medical imaging. It argues that this application-driven linear diffusion in the transformed space outperforms existing nonlinear diffusion techniques.

By Freddie {\AA}str\"om, Michael Felsberg, George Baravdish, Claes Lundstr\"om
arXiv Computer Vision
Sep 1

Mapping-Based Image Diffusion

The paper introduces a tensor‑based functional for targeted image enhancement and denoising, incorporating application‑dependent and contextual information through explicit regularization. It establishes existence of a minimizer and discusses tensor symmetry constraints, convexity, and geometric interpretation. The framework demonstrates strong performance in nonlinear scenarios like gamma correction and targeted value‑range filtering, achieving results comparable to state‑of‑the‑art PDE‑based methods.

By Freddie {\AA}str\"om, Michael Felsberg, George Baravdish
arXiv Machine Learning
Sep 24

Advances in Diffusion-Based Generative Compression

This article reviews recent diffusion‑based methods for generative lossy image compression, highlighting how these techniques encode a source into an embedding and use a diffusion model to iteratively refine the reconstruction during decoding. It discusses the role of auxiliary entropy models for transmitting the embedding, explores the use of diffusion models for information transmission via channel simulation, and frames the discussion within rate‑distortion‑perception theory, common randomness, and inverse‑problem connections. The review also identifies open challenges in the field.

By Yibo Yang, Stephan Mandt
arXiv Computer Vision
Aug 27

Learning spatially varying regularisation parameters of low regularity for image reconstruction

The article reviews how spatially adaptive regularisation weight functions can be incorporated into variational image reconstruction models like Total Variation and Total Generalised Variation. It discusses the regularity properties of these weights—constant, continuous, or piecewise constant—and how they influence edge and detail preservation. The authors highlight recent hybrid methods that learn low‑regularity weights via deep neural networks, demonstrating their effectiveness in image denoising and MRI reconstruction.

By Kostas Papafitsoros, Luca Calatroni, Andreas Kofler