# Adaptive Weighted Total Variation boosted by learning techniques in few-view tomographic imaging

arXiv: https://arxiv.org/abs/2501.09845 · HTML: https://mlsanity.com/paper/2501.09845/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [Learnable Total Variation with Lambda Mapping for Low-Dose CT Denoising](https://mlsanity.com/paper/2511.10500/) (2025)
- [Consecutive Posterior Fusion for Diffusive Recovery of Unobservable Image Structures](https://mlsanity.com/paper/2610.03261/) (2026)

## Abstract (arXiv)

This study presents the development of a spatially adaptive weighting strategy for Total Variation regularization, aimed at addressing under-determined linear inverse problems. The method leverages the rapid computation of an accurate approximation of the true image (or its gradient magnitude) through a neural network. Our approach operates without requiring prior knowledge of the noise intensity in the data and avoids the iterative recomputation of weights. Additionally, the paper includes a theoretical analysis of the proposed method, establishing its validity as a regularization approach. This framework integrates advanced neural network capabilities within a regularization context, thereby making the results of the networks interpretable. The results are promising as they enable high-quality reconstructions from limited-view tomographic measurements.