A new space-adaptive regularized constrained iterative image restoration algorithms and analysis of convergence condition

This paper proposes a new regularized constrained iterative image restoration algorithms which applies three new space-adaptive methods to a degraded image, and analyze the convergence condition of the proposed algorithm. First, we introduce space-adaptive regularization operators which change according to edge characteristics of local images in order to effectively preserve edges and boundaries in the restored images. Second, an adaptive noise reduction filter is applied on the plain regions so that salt-pepper phenomenon which results from noise amplification can be eliminated effectively. Finally, a pseudo projection operator is used to reduce the ringing artifact. And the proposed algorithm adopts momentum in the steepest descent formulation, which improves the convergence performance both in the speed and accuracy. According to the experimental results for various signal-to-noise ratios (SNR), the proposed image restoration algorithm outperforms other methods and is robust to noise effects and edge reblurring by regularization especially