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

TL;DR

This paper introduces a neural network-enhanced adaptive weighting strategy for Total Variation regularization in limited-view tomographic imaging, improving reconstruction quality without prior noise knowledge.

Contribution

It develops a neural network-based method for adaptive weighting in TV regularization, eliminating the need for iterative weight updates and prior noise information.

Findings

Enables high-quality reconstructions from limited-view data

Provides a theoretical foundation for the proposed regularization method

Achieves accurate image approximation using neural networks

Abstract

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.