Model‐guided boosting for image denoising

Boosting algorithms have demonstrated their effectiveness in improving the restoration quality of existing image denoising methods by extracting the residual signal or removing the noise leftover iteratively. Unlike existing boosting algorithms that focus on designing an ingenious recursive step by making use of the residual signal or the noise leftover, in this paper, we propose a novel model-guided boosting framework. Specifically, we derive the recursive step from an overall restoration model constructed with the technique of Regularization by Denoising (RED) towards an interpretable, extensible and flexible boosting mechanism. By using the RED, we can apply explicit regularization equipped with powerful image denoising engine to establish the global minimization problem, making the obtained model is clearly defined and well optimized. The framework enjoys the advantage of easily extending to the case of composite denoising via superadding a regularization term. As such, we develop a simultaneous model through the joint use of deep neural network and low-rank regularization to fully utilize both external and internal image properties. The resulting restoration models are capable of being flexibly solved with fixed-point strategy and steepest-descent method, leading to two types of denoising boosters. It is shown that the proposed schemes have promise results due to the improvement in signal-to-noise ratio of input signal, and are guaranteed to converge. Experiments verify the validity of the boosters for several denoising algorithms, and show that combining the power of internal and external denoising based on our framework achieves enhancement in denoising performance.