Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction
This addresses the problem of needing supervised reference images for DIP in medical imaging and image restoration, offering an incremental improvement over existing methods.
The paper tackles the overfitting and spectral bias issues in deep image prior (DIP) for image reconstruction by analyzing its training dynamics and introducing a self-guided method that jointly optimizes network weights and input without supervision. The result shows that this method outperforms original DIP and supervised methods in MR image reconstruction and DIP-based inpainting.
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.