Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction

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 …

analysis bias cs.cv eess.iv effects guidance image image restoration images imaging medical medical imaging mri overfitting popular prior quality work