Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Selective image smoothing and feature enhancement using modified shock filters(2011) Bini, A.A.; Bhat, M.S.Shock filters are widely used for image enhancement and deblurring. These filters make use of nonlinear hyperbolic Partial Differential Equations (PDEs) in order to sharpen the edges. However, in many practical cases images are corrupted by noise and other kind of degradations. Convensional shock filters are not suitable in such cases as they enhance the noise present in the image. Hence, the idea of combining shock filters with diffusion yield good results. In this paper we propose a modified "diffusion coupled shock filter." The proposed method makes use of an 'adaptive diffusion term' which limits the extent of smoothing on important edges making them more sharper. The experimental results demonstrate the efficiency of the proposed method to control diffusion and to make the reconstruction more reliable. © 2011 IEEE.Item An adaptive total variation model with local constraints for denoising partially textured images(2011) Bini, A.A.; Bhat, M.S.; Jidesh, P.Denoising algorithms such as Total Variation model modify smooth areas in images into piecewise constant patches and small scale details and textures present in the original image are not preserved satisfactorily by these processes. In this paper, we present an algorithm based on an adaptive Total Variation norm of the gradient of the image, with a family of local constraints for efficient denoising of natural images. In fact, natural images consist of smooth and textured regions. Staircase effect is reduced in smooth areas by using a modified Total Variation functional. The set of local constraints, one for each pixel in the image are able to preserve most of the fine details and textures in the images. Visual and quantitative results of proposed method are presented and are compared with results of existing methods. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).Item A fourth-order Partial Differential Equation model for multiplicative noise removal in images(IEEE Computer Society, 2013) Bini, A.A.; Bhat, M.S.In coherent imaging, the sensed images are usually corrupted with multiplicative data dependent noise. Unlike additive noise, the presence of multiplicative noise destroys the information content in the original image to a great extent. In this paper, we propose a new fourth-order Partial Differential Equation (PDE) model with a noise adaptive fidelity term for multiplicative Gamma noise removal under the variational Regularization framework. Variational approaches for multiplicative noise removal generally consist of a maximum a posteriori (MAP) based fidelity term and a Total-Variation (TV) regularization term. However, the second-order TV diffusion approximates the observed images with piecewise constant images, leading to the so-called block effect. The proposed model removes the multiplicative noise effectively and approximates observed images with planar ones making the restored images more natural compared to the second-order diffusion models. The proposed method is compared with the recent state-of-the art methods and the effective restoration capability of the filter is demonstrated experimentally. © 2013 IEEE.Item A complex diffusion driven approach for removing data-dependent multiplicative noise(2013) Jidesh, P.; Bini, A.A.In this paper we propose a second-order non-linear PDE based on the complex diffusion function. The proposed method exhibits better restoration capability of ramp edges in comparison to other second-order methods discussed in the literature. The proposed model is designed for Gamma distributed multiplicative noise which commonly appears in Ultra Sound (US) and Synthetic Aperture Radar (SAR) images. The fidelity/reactive term augmented to the complex diffusive term is derived based on the Bayesian maximum a posteriori probability (MAP) estimator as detailed in Aubert and Ajol ([10]). The regularization parameter is selected based on the noise variance of the image and thus this adaptive method helps in restoring the images at various noise variances without manually fixing the parameter. The results shown in terms of both visual and qualitative measures demonstrate the capability of the model to restore images from their degraded observations. © Springer-Verlag 2013.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.