AI RESEARCH
Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising
arXiv CS.CV
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ArXi:2604.08272v1 Announce Type: new Deep image prior (DIP) is an unsupervised deep learning framework that has been successfully applied to a variety of inverse imaging problems. However, DIP-based methods are inherently prone to overfitting, which leads to performance degradation and necessitates early stopping. In this paper, we propose a method to mitigate overfitting in DIP-based hyperspectral image (HSI) denoising by jointly combining robust data fidelity and explicit sensitivity regularization.