Structure-Adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression

Noise detection accuracy is crucial in suppressing random-valued impulse noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued impulse noise points. This paper proposes an iterative Structure-adaptive Fuzzy Estimation (SAFE) for random-valued impulse noise suppression. This SAFE method is developed in the framework of Gaussian Maximum Likelihood Estimation (GMLE). The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as noise free) is estimated via a novel Minimal Path Based Structure Propagation (MPSP) to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the re-estimation error of the uncorrupted intensity information. Comparative experiment results show that the proposed structure-adaptive fuzziness can lead to effective restoration. Efficient implementation of this SAFE method is also realized via GPU (Graphic Processing Unit)-based parallelization.