🤖 AI Summary
This work addresses the performance degradation of Deep Image Prior (DIP) in hyperspectral image denoising due to overfitting, which typically necessitates early stopping. To overcome this limitation, we propose a stable, unsupervised training scheme that eliminates reliance on early stopping by jointly integrating a smooth ℓ₁ data fidelity term and divergence-based sensitivity regularization into the DIP framework, complemented by input optimization. This synergistic design effectively suppresses overfitting and achieves superior denoising performance across real hyperspectral images corrupted by Gaussian, sparse, and stripe noise, outperforming existing DIP-based methods while enhancing both robustness and reconstruction quality.
📝 Abstract
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. The proposed approach employs a Smooth $\ell_1$ data term together with a divergence-based regularization and input optimization during training. Experimental results on real HSIs corrupted by Gaussian, sparse, and stripe noise demonstrate that the proposed method effectively prevents overfitting and achieves superior denoising performance compared to state-of-the-art DIP-based HSI denoising methods.