The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless. The DIP overfits to noise if not early stopped, or optimized via a regularized objective. We build on the regularized fine-tuning of a pretrained DIP, by adopting a novel strategy that restricts the learning to the adaptation of singular values. The proposed SVD-DIP uses ad hoc convolutional layers whose pretrained parameters are decomposed via the singular value decomposition. Optimizing the DIP then solely consists in the fine-tuning of the singular values, while keeping the left and right singular vectors fixed. We thoroughly validate the proposed method on real-measured $\mu$CT data of a lotus root as well as two medical datasets (LoDoPaB and Mayo). We report significantly improved stability of the DIP optimization, by overcoming the overfitting to noise.

翻译：深度图像先验（DIP）是一种成熟的图像重建无监督深度学习方法，但远非完美。若未提前停止或通过正则化目标进行优化，DIP会过度拟合噪声。我们基于预训练DIP的正则化微调，采用了一种将学习限制于奇异值自适应调整的新型策略。所提出的SVD-DIP使用特定设计的卷积层，这些层的预训练参数通过奇异值分解进行分解。优化DIP仅需微调奇异值，同时保持左、右奇异向量固定。我们在莲藕实测微米CT数据以及两个医学数据集（LoDoPaB和Mayo）上对该方法进行了全面验证。通过克服噪声过拟合，我们报告了DIP优化稳定性的显著提升。