# Mapping-Based Image Diffusion

arXiv: https://arxiv.org/abs/2608.29164 · HTML: https://mlsanity.com/paper/2608.29164/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 60 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [SEEDS: Superpixels Extracted via Energy-Driven Sampling](https://mlsanity.com/paper/1309.3848/) (2013)
- [On Tensor-Based PDEs and their Corresponding Variational Formulations with Application to Color Image Denoising](https://mlsanity.com/paper/2608.22302/) (2026)
- [Targeted Iterative Filtering](https://mlsanity.com/paper/2608.22299/) (2026)
- [On the Choice of Tensor Estimation for Corner Detection, Optical Flow and Denoising](https://mlsanity.com/paper/2608.22314/) (2026)

## Abstract (arXiv)

In this work, we introduce a novel tensor-based functional for targeted image enhancement and denoising. Via explicit regularization, our formulation incorporates application dependent and contextual information using first principles. Few works in literature treat variational models that describe both application dependent information and contextual knowledge of the denoising problem. We prove the existence of a minimizer and present results on tensor symmetry constraints, convexity, and geometric interpretation of the proposed functional. We show that our framework excels in applications where nonlinear functions are present such as in gamma correction and targeted value range filtering. We also study general denoising performance where we show comparable results to dedicated PDE-based state of the art methods.