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

• Published in: IEEE transactions on circuits and systems for video technology Vol. 28; no. 2; pp. 414 - 427
• Main Authors: Yang Chen, Yudong Zhang, Huazhong Shu, Jian Yang, Limin Luo, Coatrieux, Jean-Louis, Qianjin Feng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Bioengineering; Fuzzy systmes; Life Sciences; Maximum likelihood estimation; Noise; Noise measurement; Noise propagation; Noise reduction; Pixels; Random-valued impulse noise; Reliability; reliability metric; Restoration; similarity metric; structure-adaptive fuzzy estimation (SAFE); structure-adaptive fuzzy estimation (SAFE); reliability metric; similarity metric; Random-valued impulse noise
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2016.2615444

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. 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 to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the reestimation error of the uncorrupted intensity information. The comparative experimental results show that the proposed structure-adaptive fuzziness can lead to effective restoration. An efficient implementation of this SAFE method is also realized via graphics-processing-unit-based parallelization.