Faculty Publications

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Author: search.filters.author.Febin, I.P.Subject: search.filters.subject.De-noising

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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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    A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise
    (Institute of Electrical and Electronics Engineers, 2020) Febin, I.P.; Padikkal, P.; Bini, A.A.
    Remotely sensed images are widely used in many imaging applications. Images captured under adverse atmospheric conditions lead to degraded images that are contrast deficient and noisy. This study is intended to address these defects of remotely sensed data efficiently. A perceptually inspired variational model is designed based upon the Bayesian framework, powered by the retinex theory. The atmospheric noise or the shot noise (precisely following a Poisson distribution) and contrast inhomogeneity are addressed in this article. The model thus designed is tested and verified both visually and quantitatively using various test data under different statistical measures. The comparative study reveals the efficiency of the model. © 2020 IEEE.