Faculty Publications
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Item Real-time hard and soft shadow compensation with adaptive patch gradient Pairs(Springer Verlag service@springer.de, 2014) Muthukumar, M.; Nallaperumal, K.; Perumalsamy, P.; Shashikala, S.; Devi, S.; Selva Kumar, S.; Subban, R.This research emphasizes an approach toward real-time shadow compensation for dark/thick/hard and shallow/thin/soft shadows of captured scenes. While humans are very good at estimating objects size, position, color, environmental changes, movements irrespective of occlusions and noise and hence, are able to smoothly visualize the scene. But, computing machines often lack the ability to sense their environment, in a manner comparable to humans. This discrepancy prevents the automation of certain real-time jobs and the shadows make it more cumbersome. Therefore, enhancement of shadow detected region patches with suitable compensation might change object detection and scene visualization more plausible. The authors examine the patch in shadow and non-shadow regions and make the best similar patch pair. These pair characteristics are used to reconstruct both soft and hard shadow regions. However, the hard shadows do not have scene information below the shadow area, that is filled with adaptive gradient patch in-painting technique using close neighboring information. This proposed hybrid framework shows improvement in the overall image quality in terms of both qualitative and qualitative evaluations. © Springer International Publishing Switzerland 2014.Item Hybrid shadow restitution technique for shadow-free scene reconstruction(Springer Verlag service@springer.de, 2014) Muthukumar, M.; Nallaperumal, K.; Subban, R.; Perumalsamy, P.; Shashikala, S.; Selva Kumar, S.; Devi, S.Shadows are treated as a noise in computer vision scenario, even though it may found useful in many applications. This research focuses the insignificant shadow restitution methodology to improve the scene visibility and to support the dynamic range reduction. The Hybrid technique combines the physical, geometric, textural, spatial and photometric features for shadow detection. Using feature importance statistics the appropriate criteria is chosen and applied. The experiments over wide dataset prove that the proposed hybrid technique outperforms peer research proposals with the expense of computational cost and time. The output results in a shadow-free, visually plausible high quality image. © Springer International Publishing Switzerland 2014.Item Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay(2009) Narasimhan, S.V.; Harish, M.; Haripriya, A.R.; Basumallick, N.This paper proposes a new harmonic wavelet transform (HWT) based on discrete cosine transform (DCTHWT) and its application for signal or image compression and subband spectral estimation using modified group delay (MGD). Further, the existing DFTHWT has also been explored for image compression. The DCTHWT provides better quality decomposed decimated signals, which enable improved compression and MGD processing. For signal/image compression, compared to the HWT based on DFT (DFTHWT), the DCTHWT reduces the reconstruction error. Compared to DFTHWT for the speech signal considered for a compression factor of 0.62, the DCTWHT provides a 30% reduction in reconstruction error. For an image, the DCTHWT algorithm due to its real nature, is computationally simple and more accurate than the DFTHWT. Further compared to Cohen-Daubechies-Feauveau 9/7 biorthogonal symmetric wavelet, the DCTHWT, with its computational advantage, gives a better or comparable performance. For an image with 6.25% coefficients, the reconstructed image by DFTHWT is significantly inferior in appearance to that by DCTHWT which is reflected in the error index as its values are 3.0 and 2.65%, respectively. For spectral estimation, DCTHWT reduces the bias both in frequency (frequency resolution) and spectral magnitude. The reduction in magnitude bias in turn improves the signal detectability. In DCTHWT, the improvement in frequency resolution and the signal detectability is not only due to good quality DCT subband signals but also due to their stretching (decimation) in the wavelet transform. The MGD reduces the variance while preserving the frequency resolution achieved by DCT and decimation. In view of these, the new spectral estimator facilitates a significant improvement both in magnitude and frequency bias, variance and signal detection ability; compared to those of MGD processing of both DFT and DCT fullband and DFT subband signals. © Springer-Verlag London Limited 2008.Item A time-dependent switching anisotropic diffusion model for denoising and deblurring images(2012) Padikkal, P.; George, S.A conditionally anisotropic diffusion based deblurring and denoising filter is introduced in this paper. This is a time-dependent curvature based model and the steady state can be attained at a faster rate, using the explicit time-marching scheme. The filter switches between isotropic and anisotropic diffusion depending on the local image features. The switching of the filter is controlled by a binary function, which returns either zero or one, based on the underlying local image gradient features. The parameters in the proposed filter can be fine-tuned to get the desired output image. The filter is applied to various kinds of input test images and the response is analyzed. The filter is found to be effective in the reconstruction of partially textured, textured, constant-intensity and color images, as is evident from the results provided. © 2011 Copyright Taylor and Francis Group, LLC.Item Gauss curvature-driven image inpainting for image reconstruction(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2014) Padikkal, P.; George, S.In this paper, we propose a third-order Gauss curvature-driven geometric diffusion Partial Differential Equation for inpainting and reconstructing images. In Gauss curvature-driven diffusion processes, the rate of diffusion is directly proportional to the Gauss curvature value of the level curve. Since the Gauss curvature is the product of principal curvatures, its value become zero when even one of the principal curvatures is zero. Therefore, when Gauss curvature is used as a driving function for diffusion, the evolution preserves some of the meaningful structures with nonzero mean curvature values (viz. curvy edges, corners, etc.). However, the noise features always have nonzero Gauss curvature value and hence the diffusion process effectively removes them. The inpainting property of geometric PDE based on the Gauss curvature is being used in this work for reconstructing lost or degraded information. A filter is proposed to reconstruct the original images from the observed blurred and noisy images along with inpainting the desired image domain. © 2014 Copyright The Chinese Institute of Engineers.Item A nonlinear level set model for image deblurring and denoising(Springer Verlag service@springer.de, 2014) Bini, A.A.; Bhat, M.S.Image deblurring and denoising are fundamental problems in the field of image processing with numerous applications. This paper presents a new nonlinear Partial Differential Equation (PDE) model based on curve evolution via level sets, for recovering images from their blurry and noisy observations. The proposed method integrates an image deconvolution process and a curve evolution based regularizing process to form a reaction-diffusion PDE. The regularization term in the proposed PDE is a combination of a diffusive image smoothing term and a reactive image enhancement term. The diffusive and reactive terms present in the model lead to effective suppression of noise with sharp restoration of image features. We present several numerical results for image restoration, with synthetic and real degradations and compare it to other state-of-the-art image restoration techniques. The experiments confirm the favorable performance of our method, both visually and in terms of Improvement in Signal-to-Noise-Ratio (ISNR) and Pratt's Figure Of Merit (FOM). © 2013 Springer-Verlag Berlin Heidelberg.Item A convex regularization model for image restoration(Elsevier Ltd, 2014) Padikkal, P.Many variational formulations are introduced over the last few years to handle multiplicative data-dependent noise. Some of these models seek to minimize the Total Variation (TV) norm of the absolute gradient function subject to given constraints. Since the TV-norm (well-defined in the space of bounded variations (BVs)) minimization eventually results in the formation of piece-wise constant patches during the evolution process, the filtered output appears blocky. In this work the block effect (commonly known as staircase effect) is being handled by using a convex combination of TV and Tikhonov filters, which are defined in BV and L2 (square-integrable functions) spaces, respectively. The constraint for the minimizing functional is derived based on a maximum a posteriori (MAP) regularization approach, duly considering the noise distributions. Therefore, this model is capable of denoising speckled images, whose intensity is Gamma distributed. The results are demonstrated both in terms of visual and quantitative measures. © 2014 Elsevier Ltd. All rights reserved.Item Experimental study for the assessment of suitability for vegetation growth on coal mine overburden(Inderscience Enterprises Ltd., 2015) Ram Chandar, K.R.; Chaitanya, V.; Raghunandan, M.E.Owing to increased production and productivity of opencast coal mines, large amount of waste rock is removed and stacked in the form of waste dumps. Positive utilisation of such waste rock not only saves considerable dumping land but also reduces problem of maintaining stable and environmentally friendly dumps. One of the major utilisation of waste rock is to use it for vegetation. Therefore a systematic investigation to study the fertile characteristics of overburden waste rock for vegetation was conducted. Waste rock samples collected from an opencast coal mine dump in South India were used in the laboratory experiments. Observations showed the suitability of mine wastes for vegetation when supplemented with additives/nutrients - bottom ash, fly ash, lime stone powder and secondary sludge from sewage treatment plants (STPs) were considered for this purpose as additives. Results suggest that the mine-overburden supplemented with sewage waste (atleast 25% by volume) to be suitable for effective vegetation. © © 2015 Inderscience Enterprises Ltd.Item Image Restoration Using Adaptive Region-Wise p-Norm Filter with Local Constraints(World Scientific, 2016) Bini, A.A.; Padikkal, P.In this work, we introduce a feature adaptive second-order p-norm filter with local constraints for image restoration and texture preservation. The p-norm value of the filter is chosen adaptively between 1 and 2 in a local region based on the regional image characteristics. The filter behaves like a mean curvature motion (MCM) [A. Marquina and S. Osher, SIAM Journal of Scientific Computing 22, 387-405 (2000)] in the regions where the p-norm value is 1 and switches to a Laplacian filter in the rest of the regions (where the p-norm value is 2). The proposed study considerably reduces stair-case effect and effectively removes noise from images while deblurring them. The noise is assumed as Gaussian distributed (with zero mean and variance ?2) and blur is linearly shift invariant (out-of-focus). The filter converges at a faster rate with semi-implicit Crank-Nicholson scheme. The regularization parameter is initialized and updated based on the local image features and therefore this filter preserves edges, structures, textures and fine details present in images very well. The method is applied on different kinds of images with different image characteristics. We show the response of the filter to various kinds of images and numerically quantify the performance in terms of standard statistical measures. © 2016 World Scientific Publishing Company.Item Image despeckling and deblurring via regularized complex diffusion(Springer London, 2017) Padikkal, P.; Bini, A.A.In this paper an image restoration and enhancement model is being proposed, which is suitable for multiplicative data-dependent speckle noise (whose intensity is Gamma distributed) under linear shift-invariant blurring artifacts. The proposed strategy devises a nonlinear second-order diffusive-reactive model for enhancing and restoring images degraded by the aforementioned scenario. The reactive term is derived based on the Maximum a posteriori (MAP) estimator, to make it adaptive to the noise distribution in the input data. This noise-adaptive reactive term helps to restore and enhance the images under data-correlated noise setup. Unlike the other second-order nonlinear diffusion methods, the proposed solution preserves edges and details and reduces piecewise constant approximation in the homogeneous intensity regions in the course of its evolution. The experimental results demonstrated in this paper duly support the above claims. © 2017, Springer-Verlag London.