SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processing
Ziyu Shu Zhixin Pan
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 prior (SDIP) as an improved version of the original DIP. We observed that
the changes in the DIP networks' input and output are highly correlated during
each iteration. SDIP efficiently utilizes this trait in a reinforcement
learning manner, where the current iteration's output is utilized by a steering
algorithm to update the network input for the next iteration, guiding the
algorithm toward improved results. Experimental results across multiple
applications demonstrate that our proposed SDIP framework offers improvement
compared to the original DIP method and other state-of-the-art methods.