Faculty Publications
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Publications by NITK Faculty
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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 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.Item 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.Item 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.Item Multiple-Coil Magnetic Resonance Image Denoising and Deblurring With Nonlocal Total Bounded Variation(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2020) Holla Kayyar, K.S.; Padikkal, P.; Bini, A.A.One of the complex tasks in image restoration is to restore images under data correlated noise contaminations. In real-time medical imaging scenarios, such as Magnetic Resonance (MR), Ultrasound, Computed Tomography(CT) etc, it is observed that, the data of interest is severely degraded with data dependent noise interventions. A Nonlocal Total Bounded Variation (NLTBV) approach is being proposed in this paper to denoise as well as deblur multiple-coil MR images corrupted by non-central Chi distributed noise and linear Gaussian blur. The energy functional for the restoration model is derived by applying the Maximum A Posteriori (MAP) estimator on the Probability Density Function (PDF) of the non-central Chi distribution. The numerical implementation is performed using the split-Bregman iterative scheme to improve the convergence rate. The proposed model is compared with the other state of the art models in terms of both visual and statistical quantifications to demonstrate it's performance. © 2019, © 2019 IETE.Item A self-attention driven retinex-based deep image prior model for satellite image restoration(Elsevier Ltd, 2024) Shastry, A.; Padikkal, J.; George, S.; Bini, A.A.A self attention driven Deep Image Prior (DIP) framework has been proposed in this work for restoring satellite images corrupted by speckled interference and contrast deficiency. The retinex-based framework incorporated here-in leverages the benefits of DIP approach for image restoration, thus requiring only a single input image, eliminating the need for ground truth or training data. An attention framework is incorporated into the architecture of DIP networks to effectively capture fine textures, enhancing the restoration capability of the model. Two generative networks are employed to obtain the luminance and reflectance maps, with the model's loss functions specifically designed to tackle speckle interference and contrast distortions present in the input. These generated maps eventually reconstruct the enhanced version of the image. Satellite images from different sensors are used to demonstrate and compare the performance of the model. Various state-of-the-art models are evaluated and compared with the proposed strategy using different image quality metrics and statistical tests. The experimental results, incorporating both visual and statistical inferences, demonstrate the superiority and efficiency of the model. Additionally, an ablation analysis is performed to determine optimal regularization parameters, and the significance of integrating attention modules at different architecture layers is also demonstrated. © 2023 Elsevier LtdItem A weighted nuclear norm (WNN)-based retinex DIP framework for restoring aerial and satellite images corrupted by gamma distributed speckle noise(Springer, 2024) Shastry, A.; Padikkal, J.; George, S.; Bini, A.A.Restoration and enhancement are crucial preprocessing steps in the satellite domain. Mainly in active remote sensing such as Synthetic Aperture Radar (SAR), the images are more prone to speckle distortions and their reduction is not so trivial. Traditional deep learning models require large training datasets, limiting their applicability. This paper introduces a novel approach that combines the Deep Image Prior (DIP) model with a weighted nuclear norm (WNN) within a variational retinex framework to address these challenges. DIP leverages prior knowledge about noise distribution and works effectively with a single noisy image, eliminating the need for a large number of training images or ground truth. The WNN assigns non-negative weights to singular values, capturing the significance of each value and preserving crucial information during restoration. This approach offers a promising solution for satellite image restoration without relying on huge training data. The proposed method is evaluated through extensive experiments using various image quality metrics, including PSNR, SSIM, ENL, CNR, Entropy, and GCF. The comparative studies provide compelling evidence that the proposed method surpasses existing techniques in effectively restoring and enhancing speckled input images. Furthermore, statistical analysis performed using the Friedman test demonstrates the superior denoising performance of the model. Additionally, an ablation study is conducted to empirically determine the optimal regularization parameters, ensuring the optimal performance of the model. However, the theoretical selection of parameters for achieving optimal results remains an area that requires further exploration. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.Item AttentionDIP: attention-based deep image prior model to restore satellite and aerial images from gamma distributed speckle interference(Springer Science and Business Media Deutschland GmbH, 2024) Shastry, A.; George, S.; Bini, A.A.; Padikkal, J.Image restoration is an inevitable pre-processing step in most satellite imaging applications. The satellite imaging modality such as Synthetic Aperture Radar (SAR) is prone to speckle distortions due to constructive and destructive interference of the probing signals. Speckles being data correlated and multiplicative, their reduction is not so trivial. Since speckles are not purely noise interventions, a blind reduction process leads to spurious analysis at the later stages. Moreover, the image details are liable to get compromised during such a noise reduction process. An attention-based deep image prior (DIP) model with U-Net architecture has been proposed in this work to carefully address these setbacks. The attention block is used to scale the features extracted from the encoder, and they are concatenated with the features from the decoder to obtain both low- and high-level features. The attention module incorporated in the model helps to extract significant complex structures in SAR images. Further, the DIP model duly respects the noise distribution of speckles while performing the despeckling task. Various synthetic, natural, aerial, and satellite images are subjected to the testing and verification process, and the results obtained are in favor of the proposed model. The quantitative analysis carried out using various statistical metrics in this study also reveals the restoration ability of the proposed method in terms of both despeckling and structure preservation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.Item A retinex inspired deep image prior model for despeckling and deblurring of aerial and satellite images using proximal gradient method(Taylor and Francis Ltd., 2025) Shastry, A.; Bini, A.A.; Padikkal, J.Unsupervised learning models, particularly in the remote sensing domain, have gained significant attention in recent years. Various degradations in the satellite images, primarily occurring during acquisition, pose a substantial hurdle in obtaining reliable ground truth and extensive training data. The Deep Image Prior model (DIP) addresses these issues by performing restoration tasks using a single image, relying on the implicit regularization inherent in the network architecture. In this paper, we propose a novel approach, integrating the DIP model within the retinex framework to restore aerial and satellite images from the Gamma distributed speckles and linear shift-invariant Gaussian blur along with contrast enhancement using the alternating proximal gradient descent ascent (PGDA) method. Our proposed methodology combines implicit regularization with explicit total variational (TV) regularization, incorporating automated estimation of local regularization parameters. The data-fidelity component in the optimization function is formulated using the Bayesian Maximum A posteriori (MAP) estimate, assuming the speckles follow the Gamma distribution. Demonstration of despeckling and deblurring alone and in addition as a combined task is carried out on aerial and Synthetic Aperture Radar (SAR) images with different resolutions and polarization from various sources. Results obtained are compared with various state-of-the-art despeckling and deblurring models using distinct image quality metrics such as Equivalent Number of Looks (ENL), Contrast to Noise Ratio (CNR), Edge Preserving Index (EPI), Entropy, Global Contrast Factor (GCF), Natural Image Quality Evaluator (NIQE), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Bradley-Terry (B-T) score based on the various factors. The quality of restored images depicted superior performance of the proposed method over the existing models under study. © 2024 Informa UK Limited, trading as Taylor & Francis Group.