Faculty Publications
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Publications by NITK Faculty
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Item Steganalysis: Using the blind deconvolution to retrieve the hidden data(2011) Jidesh, P.; George, S.Steganography has gained a substantial attention due to its application in wide areas. Steganography as it literally mean is hiding the information (stego data) inside the data (communication data) so that the receiver can only extract the desired information from the data. Steganalysis is the reverse process of steganography in which the information about the original data is hardly available, from the received data the extractor needs to identify the original data. Since this belong to a class of inverse problems it is hard to find the approximate match of the original data from the received one. In most of the cases this will fall under the category of ill-posed problems. The stego-data that has been embedded into the communication data can be considered as linear bounded operator operating on the input data and the reverse process (the Steganalysis) can be thought like a deconvolution problem by which we can extract the original data. Here we are assuming the watermarking as a linear operation with a bounded linear operator K : X→Y where X and Y are spaces of Bounded Variation (BV). The forward problem (the Steganography) is a direct convolution and the reverse (backward) problem (steganalysis) is a de-convolution procedure. In this work we are embedding a Gaussian random variable array with zero mean and with a specific variance into the data and we show how the original data can be extracted using the regularization method. The results are shown to substantiate the ability of the method to perform steganalysis. © 2011 IEEE.Item Non-local Gradient Fidelity Model for Multiplicative Gamma Noise Removal(Institute of Electrical and Electronics Engineers Inc., 2018) Banothu, B.; Jidesh, P.In this paper a non-local gradient vector flow model is designed for restoration of images corrupted with Gamma distributed (speckle) noise and linear blurring artefacts. The filter effectively preserves edges and finer details in the course of its evolution due to the presence of the non-local TV based diffusion term and the piecewise linear approximation is reduced considerably by the gradient fidelity term present in the model. The model is found suitable for restoration of various images from the field of satellite and clinical imaging. The experimental results are shown and compared for different image data sets both visually and qualitatively using various statistical measures. © 2017 IEEE.Item A Graph Spectral Approach for Restoring Images Corrupted by Shot-Noise(Springer Science and Business Media Deutschland GmbH, 2021) Jidesh, P.; Bini, A.A.Image restoration is a fundamental problem in image processing. Usually, images gets deteriorated while storing or transmitting them. Image restoration is an ill-posed inverse problem, wherein one has to restore the original data with a priori information or assumption regarding the degradation model and its characteristics. The literature is too elaborate for various restorations under different assumptions on the degradation-architecture. This paper introduces a strategy based on graph spectral theory to restore images with non-local filters controlled by a loss function. The non-local similarity-based weight function controls the restoration process resulting in the preservation of local image features considerably well. The parameter controlled adaptive fidelity term helps to re-orient the diffusion to handle data correlated shot-noise following a Poisson distribution, which is pretty common in many medical and telescopic imaging applications. Experimental results are conforming to the fact that the proposed model performs well in restoring images of the different intensity distributions. © 2021, Springer Nature Singapore Pte Ltd.