Image de-noising with subband replacement and fusion process using bayes estimators
- Title
- Image de-noising with subband replacement and fusion process using bayes estimators
- Author
- 김대경
- Keywords
- Dictionary learning; Wavelet transform; Sub-band replacement; Fusion estimation; Factor analysis; Image de-noising
- Issue Date
- 2018-08
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Citation
- COMPUTERS & ELECTRICAL ENGINEERING, v. 70, Page. 413-427
- 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. (C) 2017 Elsevier Ltd. All rights reserved.
- URI
- https://www.sciencedirect.com/science/article/pii/S0045790616303378https://repository.hanyang.ac.kr/handle/20.500.11754/81235
- ISSN
- 0045-7906
- DOI
- 10.1016/j.compeleceng.2017.05.023
- Appears in Collections:
- COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY[E](과학기술융합대학) > APPLIED MATHEMATICS(응용수학과) > Articles
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