Smitha, A.Febin, I.P.Padikkal, P.2026-02-042022Biomedical Signal Processing and Control, 2022, 71, , pp. -17468094https://doi.org/10.1016/j.bspc.2021.103234https://idr.nitk.ac.in/handle/123456789/22868A 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 LtdData visualizationImage reconstructionOptical tomographyRestorationSpeckleDe-specklingDistributed noiseGamma distributed noiseGamma-distributedGeneralized variationNon-local total generalized variationNonlocalOptical coherence tomography image enhancementRetinexRetinex frameworkImage enhancementarticlecontrolled studyimage artifactimage enhancementimage reconstructionnoiseoptical coherence tomography