Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep convolutional neural network, and learns the network's parameters such that the output matches the corrupted observation. Despite its impressive reconstructive properties, the approach is slow when compared to supervisedly learned, or traditional reconstruction techniques. To address the computational challenge, we bestow DIP with a two-stage learning paradigm: (i) perform a supervised pretraining of the network on a simulated dataset; (ii) fine-tune the network's parameters to adapt to the target reconstruction task. We provide a thorough empirical analysis to shed insights into the impacts of pretraining in the context of image reconstruction. We showcase that pretraining considerably speeds up and stabilizes the subsequent reconstruction task from real-measured 2D and 3D micro computed tomography data of biological specimens. The code and additional experimental materials are available at https://educateddip.github.io/docs.educated_deep_image_prior/.

翻译：深度图像先验（DIP）近期被提出作为一种有效的无监督图像复原方法。DIP将待恢复图像表示为深度卷积神经网络的输出，并通过学习网络参数使输出与退化观测一致。尽管其具有出色的重建性能，但与有监督学习或传统重建技术相比，该方法运行速度较慢。为解决这一计算挑战，我们为DIP赋予两阶段学习范式：（i）在模拟数据集上对网络进行有监督预训练；（ii）微调网络参数以适应目标重建任务。我们通过详尽的实证分析揭示了预训练在图像重建中的影响机理。实验表明，预训练显著加速并稳定了基于生物样本真实测量二维与三维微CT数据的后续重建任务。相关代码及补充实验材料见https://educateddip.github.io/docs.educated_deep_image_prior/。