- The paper introduces DeepRED, which synergizes Deep Image Prior and Regularization by Denoising using ADMM to significantly enhance unsupervised image recovery.
- It leverages explicit denoising as a regularizer to complement the implicit neural architecture of DIP, thereby boosting performance in inverse imaging problems.
- Experimental evaluations in denoising, super-resolution, and deblurring demonstrate DeepRED’s advantage over both unsupervised and leading supervised methods.
DeepRED: Synergizing Deep Image Prior and Regularization by Denoising
The paper introduces DeepRED, a novel approach for enhancing the results of Deep Image Prior (DIP), a method presented by Ulyanov et al. that leverages deep-learning architectures to tackle inverse problems in imaging in an unsupervised manner. Despite the inherent promise and demonstrated effectiveness of DIP in scenarios involving denoising, inpainting, and super-resolution, its outcomes have not consistently matched those of state-of-the-art methods. This paper aims to refine DIP by integrating Regularization by Denoising (RED), thereby enriching the regularization to yield superior image recovery outcomes.
Merging DIP and RED
Deep Image Prior operates by removing explicit regularization, usually intrinsic to inverse problem formulations, and instead uses the structure of a neural network as an implicit regularizer. This is done by constraining the image reconstruction such that it is the output of a trained neural network, where the network parameters are fitted to minimize the discrepancy between the corrupted observation and the modeled image output. The challenge addressed by this work is to introduce an explicit regularization, synergizing it with the implicit component provided by DIP—implemented via RED—to boost the efficacy of DIP without compromising its unsupervised nature.
Regularization by Denoising brings into play robust denoising algorithms already established in the field, thus transforming them into a regularization tool. RED defines a regularization term that formulates a denoising function within an energy minimization framework, producing a gradient that alleviates the need to differentiate the denoiser directly—often the computational hurdle—while impressively showing convexity under certain conditions.
Technical Scheme and Numerical Insights
The paper demonstrates the union of DIP and RED in a comprehensive manner, executing the optimization using the Alternating Direction Method of Multipliers (ADMM). ADMM provides a practical pathway to manage the complex non-linear system involved in the deep neural network's architecture, solving the combined objective by iteratively correcting the parameters and image estimates. This is facilitated without explicit differentiation of the denoiser, saving computational resources and ensuring updates that reduce complexity.
Through experimental validation, several inverse problems, namely image denoising, single image super-resolution (SISR), and image deblurring, are rigorously tested to illustrate the advantages of the proposed approach. DeepRED consistently surpasses not only DIP but also RED and unsupervised alternatives, achieving closer performance to noteworthy supervised methods like LapSRN and MSWNN.
Implications and Future Directions
DeepRED suggests substantial practical implications, especially in image restoration where standard supervised learning paradigms might not be feasible. The utilization of unsupervised strategies like DeepRED has particular appeal in scenarios where labeled data is scarce or non-existent. The impressive numerical stability and performance improvement imply potential across diverse fields reliant on high-quality image reconstruction.
Further research could aim to address the computational demands of DeepRED by exploring optimization strategies that speed convergence without loss of quality. Additionally, pursuing the integration of high-performance denoisers, potentially those also derived from deep-learning architectures, might pave the way for even sharper results. Ultimately, understanding the relationship between architectural choices in DIP and the resulting perceptual quality could significantly influence the design of future unsupervised image recovery algorithms.
DeepRED represents a significant stride in the domain of inverse problems, offering a composite solution that seamlessly combines the innovative spirit of DIP with the solid ground of established denoising techniques through RED, achieving both theoretical expansion and practical rendering of enhanced image quality.