SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processing

arXiv:2404.12142v1 Announce Type: cross
Abstract: Deep image prior (DIP) proposed in recent research has revealed the inherent trait of convolutional neural networks (CNN) for capturing substantial low-level image statistics priors. This framework efficiently addresses the inverse problems in image processing and has induced extensive applications in various domains. However, as the whole algorithm is initialized randomly, the DIP algorithm often lacks stability. Thus, this method still has space for further improvement. In this paper, we propose the self-reinforcement deep image …

abstract applications arxiv cnn convolutional neural networks cs.cv cs.lg domains eess.iv framework however image image processing low networks neural networks prior processing reinforcement research statistics type