# Zero-shot Bias Correction: Efficient MR Image Inhomogeneity Reduction Without Any Data

arXiv: https://arxiv.org/abs/2501.12244 · HTML: https://mlsanity.com/paper/2501.12244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 2 of 2)

- [Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation](https://mlsanity.com/paper/2103.00860/) (2021)
- [Unsupervised Decomposition Networks for Bias Field Correction in MR Image](https://mlsanity.com/paper/2307.16219/) (2023)

## Built on by (most cited first, 1 of 1)

- [Structure and Smoothness Constrained Dual Networks for MR Bias Field Correction](https://mlsanity.com/paper/2507.01326/) (2025)

## Abstract (arXiv)

In recent years, deep neural networks for image inhomogeneity reduction have shown promising results. However, current methods with (un)supervised solutions require preparing a training dataset, which is expensive and laborious for data collection. In this work, we demonstrate a novel zero-shot deep neural networks, which requires no data for pre-training and dedicated assumption of the bias field. The designed light-weight CNN enables an efficient zero-shot adaptation for bias-corrupted image correction. Our method provides a novel solution to mitigate the biased corrupted image as iterative homogeneity refinement, which therefore ensures the considered issue can be solved easier with stable convergence of zero-shot optimization. Extensive comparison on different datasets show that the proposed method performs better than current data-free N4 methods in both efficiency and accuracy.