A survey on Deep Image Prior for image denoising

Abstract

Deep Image Prior (DIP) has gained attention as a promising approach that bridges traditional hand-crafted priors and deep learning-based models. By utilizing a convolutional neural network (CNN) structure with "zero sample" training, DIP effectively learns the priors of degraded images, primarily for image reconstruction. Despite its potential, there is a lack of comprehensive reviews summarizing the various DIP-based image denoising methods. This paper aims to fill this gap by providing an overview of DIP-based image denoising approaches by reviewing recent papers on the topic. We classify these methods into four groups based on their enhancement strategies: theoretical investigations, network structure, network input, and loss function. The review evaluates the strengths and weaknesses of DIP-based methods, compares state-of-the-art variants, and analyzes the impact of various improvements on denoising performance. Additionally, we identify the challenges in applying DIP to image denoising and suggest directions for future research. This review provides valuable insights into the potential of DIP in image processing, especially for those new to unsupervised deep learning models.