通过正则化与优化策略，有效防止深度图像先验在高光谱去噪中过拟合。
Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising
- 融合平滑L1数据项与基于散度的正则化，抑制过拟合。
- 在真实高光谱图像上对高斯、稀疏、条纹噪声均实现更优去噪效果。
- 适合处理噪声复杂的真实高光谱图像，尤其适合无配对训练数据场景。
深度图像先验（DIP）是一种成功的无监督深度学习框架，已广泛应用于各类逆向成像问题。然而，基于DIP的方法易发生过拟合，导致性能下降，需依赖早停机制。本文提出一种新方法，通过联合使用鲁棒数据保真项与显式敏感性正则化，缓解基于DIP的高光谱图像（HSI）去噪中的过拟合问题。该方法结合平滑ℓ₁数据项、基于散度的正则化，并在训练中引入输入优化。在受高斯、稀疏及条纹噪声污染的真实高光谱图像上的实验表明，所提方法能有效防止过拟合，且相比现有最先进基于DIP的高光谱去噪方法，取得了更优的去噪性能。
原文摘要 · Abstract (English)
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.
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