Faculty Publications

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Author: search.filters.author.Febin, I.P.Subject: search.filters.subject.Image reconstruction

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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.
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    A Perceptually Inspired Variational Model for Enhancing and Restoring Remote Sensing Images
    (Institute of Electrical and Electronics Engineers Inc., 2021) Padikkal, P.; Febin, I.P.
    Perceptually inspired algorithms have captured the recent attention of scientists and engineers due to their inherent capability to enhance the contrast of images, especially from the remote sensing domain. In this letter, we propose a perceptually inspired retinex model relying on the variational framework for enhancing and denoising satellite images captured by various imaging devices. A variational framework incorporates priors and data fidelity aspects in the designed functional, whose optimized solution yields the desired output. The model respects the distribution of the noise while enhancing the data. The overall performance is demonstrated using the visual and quantitative measures. © 2004-2012 IEEE.
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    A retinex based non-local total generalized variation framework for OCT image restoration
    (Elsevier Ltd, 2022) Smitha, A.; Febin, I.P.; Padikkal, P.
    A retinex driven non-local total generalized variational (TGV) model is proposed in this paper to restore and enhance speckled images. The combined first and second-order TGV controlled by a balancing parameter are used to improve the enhancement and restoration process. The distribution of the speckle is estimated from input images using detailed statistical analysis. The model is designed to handle speckle-noise following a Gamma distribution, as analyzed later in this paper. The non-local TGV model is shown to restore images without causing any visual artefacts, unlike the normal total variation (TV) model. Moreover, a retinex framework shows a remarkable improvement to the contrast features of the data without distorting the natural image characteristics as quantified visually and statistically in the experimental section of this work. A fast numerical approximation based on the Split-Bregman scheme is employed to improve the efficiency of the model in terms of computation. The proposed model is verified to have despeckled and enhanced the Optical Coherence Tomography (OCT) data to a greater extent compared to the state-of-the-art models as observable from the results shown in this paper. © 2021 Elsevier Ltd