Image de-noising with subband replacement and fusion process using bayes estimators,. (August 2018)

• Record Type: Journal Article
• Authors: Muhammad, Nazeer; Bibi, Nargis; Wahab, Abdul; Mahmood, Zahid; Akram, Tallha; Naqvi, Syed Rameez; Oh, Hyun Sook; Kim, Dai-Gyoung
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 413
• Page End: 427
• Publication Date: 2018-08
• Subjects: Dictionary learning -- Wavelet transform -- Sub-band replacement -- Fusion estimation -- Factor analysis -- Image de-noising; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.05.023
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: A hybrid image de-noising framework with an automatic parameter selection scheme is proposed to handle substantially high noise with an unknown variance. The impetus of the framework is to preserve the latent detail information of the noisy image while removing the noise with an appropriate smoothing and feasible sharpening. The proposed method is executed in two steps. First, the sub-band replacement and fusion process based on accelerated version of the Bayesian non local means method are implemented to enhance the weak edges that often result in low gradient magnitude and fade out during the de-noising process. Then, a truncated beta-Bernoulli process is employed to infer an appropriate dictionary of the edge enhanced data to obtain de-noising results precisely. Numerical simulations are performed to substantiate the restoration of the weak edges through sub-band replacement and fusion process. The proposed de-noising scheme is validated through visual and quantitative results using well established metrics.