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Panchromatic image denoising by a log-normal-distribution-based anisotropic diffusion model
, M. Malarvel
Published in SPIE
2018
Volume: 13
   
Issue: 1
Abstract
An anisotropic diffusion model based on a log-normal distribution of a local gray-level is used to propose a way to denoise the panchromatic images. The implication of the low-edge gradient of the feature space for denoising and smoothing the noisy image is adaptively adjusted by the adaptive threshold parameter in a diffusion coefficient function. Furthermore, to terminate the diffusion process, an entropy-based stopping criterion is implemented. The proposed model is compared with the existing models such as Perona-Malik (PM), adaptive PM, difference eigenvalue PM, modified PM, and Maiseli-Gao. In order to analyze the performance of the models, quantitative metrics such as standard deviation, entropy, and the signal-to-noise ratio of a two-dimensional line profile are used. For further analysis, the results of denoising models are segmented using entropy-based segmentation techniques such as Harvda, Renyi, Kapur, and Yen models. A misclassification error metric is used to evaluate the segmentation results. The metric results show that the proposed model effectively removes the noise and preserves the features of a panchromatic image. © 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
About the journal
JournalData powered by TypesetJournal of Applied Remote Sensing
PublisherData powered by TypesetSPIE
ISSN19313195