Faculty Publications

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Author: search.filters.author.Febin, I.P.Subject: search.filters.subject.Noise estimation

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    Noise classification and automatic restoration system using non-local regularization frameworks
    (Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Febin, I.P.; Padikkal, P.; Bini, A.A.
    Medical, satellite or microscopic images differ in the imaging techniques used, hence their underlying noise distribution also are different. Most of the restoration methods including regularization models make prior assumptions about the noise to perform an efficient restoration. Here we propose a system that estimates and classifies the noise into different distributions by extracting the relevant features. The system provides information about the noise distribution and then it gets directed into the restoration module where an appropriate regularization method (based on the non-local framework) has been employed to provide an efficient restoration of the data. We have effectively addressed the distortion due to data-dependent noise distributions such as Poisson and Gamma along with data uncorrelated Gaussian noise. The studies have shown a 97.7% accuracy in classifying noise in the test data. Moreover, the system also shows the capability to cater to other popular noise distributions such as Rayleigh, Chi, etc. © 2018, © 2018 The Royal Photographic Society.
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    Estimation of Noise Using Non-local Regularization Frameworks for Image Denoising and Analysis
    (Springer Verlag, 2019) Padikkal, P.; Febin, I.P.
    In this paper, we propose a novel model which adaptively estimates the noise probability distribution and noise parameters from the input image and restores the data accordingly choosing appropriate regularization model designed for it. In most imaging applications the noise characteristics are assumed prior to the restoration process. This assumption is generally based on the previous experimental study of the images from a specific modality. The adaptive detection of the noise distribution from the data makes it robust and highly suitable for automated signal and image restoration systems. The non-local framework implemented using fast numerical solvers catalyzes the convergence rate of the model. Here we analyze three different noise distributions such as Gamma, Poisson, and Gaussian. Among this Gaussian is additive and source independent, Gamma is multiplicative and source dependent, and finally Poisson is data dependent (neither multiplicative nor additive). The model can be extended to the other source-dependent distributions such as Rayleigh and Rician by appropriately tuning it. The experimental results conform to the assumption regarding the noise distribution and noise parameters estimation capability of the model. © 2018, King Fahd University of Petroleum & Minerals.