The ability of deep image prior (DIP) to recover high-quality images from incomplete or corrupted measurements has made it popular in inverse problems in image restoration and medical imaging including magnetic resonance imaging (MRI). However, conventional DIP suffers from severe overfitting and spectral bias effects.In this work, we first provide an analysis of how DIP recovers information from undersampled imaging measurements by analyzing the training dynamics of the underlying networks in the kernel regime for different architectures.This study sheds light on important underlying properties for DIP-based recovery.Current research suggests that incorporating a reference image as network input can enhance DIP's performance in image reconstruction compared to using random inputs. However, obtaining suitable reference images requires supervision, and raises practical difficulties. In an attempt to overcome this obstacle, we further introduce a self-driven reconstruction process that concurrently optimizes both the network weights and the input while eliminating the need for training data. Our method incorporates a novel denoiser regularization term which enables robust and stable joint estimation of both the network input and reconstructed image.We demonstrate that our self-guided method surpasses both the original DIP and modern supervised methods in terms of MR image reconstruction performance and outperforms previous DIP-based schemes for image inpainting.

通过分析深度图像先验（DIP）的训练动态，我们揭示了DIP基于核函数制度下从欠采样图像测量中恢复信息的重要属性；通过引入参考图像作为网络输入并结合新颖的去噪正则化项，我们提出了一种自引导重建方法，通过同时优化网络权重和输入，消除了训练数据的需求，并在磁共振图像重建方面超过了传统DIP和现代监督方法，并且在图像修补中也优于之前基于DIP的方案。

深度图像先验分析与图像重建中的自引导利用