Faculty Publications
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Item Convolutional neural networks for medical image analysis(CRC Press, 2022) Gogineni, R.; Chaturvedi, A.The enormous advancement of Deep Learning (DL) algorithms in various fields has been adopted by many perspectives of medical image analysis. In the context of deep learning, convolutional neural networks (CNNs) are widely used models for medical imaging tasks and lead to a huge progress in computer aided diagnosis. CNNs are being profoundly absorbed in the research of medical imaging because of their pertinent features in preserving local image relations and dimensionality reduction. CNNs demonstrate excellent performance in lesion detection and other vision tasks. This chapter presents the modules of convolutional neural networks and their prominent architectures employed for medical image analysis. The commonly used variants of CNNs in medical image analysis including ResNet, GoogleNet, and fully convolutional neural networks are analyzed. The principal research areas and applications of medical image registration, segmentation, detection and classification are discussed. The overview of the above mentioned tasks in a few of the usual diagnosis areas of the human body such as brain, lungs and eye is presented in this chapter. The crucial challenge in medical image analysis using CNNs is the availability of training data sets. This problem is addressed in this chapter, by providing the existing medical image datasets for different application areas like chest, diabetic retinopathy, lungs and heart. Finally, the chapter is concluded by discussing the challenges associated with CNN architectures and promising future research directions © 2023 Şaban öztürk. All rights reserved.Item Sparsity inspired pan-sharpening technique using multi-scale learned dictionary(Elsevier B.V., 2018) Gogineni, R.; Chaturvedi, A.The significant issues in remote sensing image fusion are enhancing the spatial details and preserving the essential spectral information. The classical pan-sharpening methods often incur spectral distortion and still striving to produce the fused images with prominent spatial and spectral attributes. Motivated by the desirable results of sparse representation (SR) theory, a novel pan-sharpening method is developed based on SR of high frequency (HF) components over a multi-scale learned dictionary (MSLD). MSLD technique acquires the capability of extracting the intrinsic characteristics of images, wherein, it possess the features of both multi-scale representation and learned dictionaries. In this paper, the dictionaries are adaptively learned from HF sub-images derived from the two versions of panchromatic image, realized at different spatial resolutions. A fast and computationally efficient algorithm is used for dictionary learning. The notion of SR together with patch recurrence over different scales is incorporated to estimate the high frequency details. The fused image is reconstructed by injecting the band specific spatial details into the up-sampled multi-spectral images. The performance of the proposed method is appraised with the datasets from different satellite sensors namely, QuickBird, IKONOS, WorldView-2 and Pléiades. The observations inferred from visual perception and quality indices analysis manifest the efficiency of proposed method over several well-known methods for the datasets considered at reduced-scale and full-scale resolutions. Further, the quantitative analysis of obtained performance measures confirms the efficacy of the proposed method for the reduced-scale and full-scale data sets. Especially, at a reduced-scale, proposed method yields an optimal value of Correlation coefficient, Structural similarity and Q4. In a comparative sense, usage of the proposed method at full-scale results in 4% and 2.56% improvement in the Spatial distortion index for QuickBird and WorldView-2 data respectively contrary to the best reported outcome obtained from Sparse Representation of injected details (SR-D) scheme. Invariably, for full-scale data, the QNR attains its optimal value. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)Item A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement(Institute of Electrical and Electronics Engineers, 2019) Gogineni, R.; Chaturvedi, A.Pansharpening (PS) is a prominent remote sensing image fusion technique. It yields high-resolution multispectral (HRMS) images, which are imperative for the applications, such as recognition and detection. The PS methods based on conventional sparse representation induce blurring effects and are unable to preserve the essential spatial details in the fused outcome. In this article, to overcome these drawbacks, a robust fusion scheme is proposed based on convolutional sparse coding (CSC). The source images are decomposed into its constituent texture and cartoon components. The sparse coefficient maps are acquired from texture components by adapting CSC. Texture components are fused using activity level measurement, whereas averaging mechanism is used to fuse the cartoon components. The HRMS image is reconstructed by combining the fused components in proportion to the gradient information. Impact of number of filters on quality metrics estimation is analyzed. Comprehensive experiments are performed on the images acquired from distinct sensors. The proposed method is evaluated in terms of visual analysis and the quantitative metrics with reduced-scale and full-scale experiments. Extensive evaluations manifest the capability of the proposed method of maintaining the balanced tradeoff and retaining the desired spatial and spectral details. © 2008-2012 IEEE.Item A variational pan-sharpening algorithm to enhance the spectral and spatial details(Taylor and Francis Ltd., 2021) Gogineni, R.; Chaturvedi, A.; Daya Sagar, B.S.Pan-sharpening is a remote sensing image fusion technique that generates a high-resolution multispectral (HRMS) image on combining a low resolution multispectral (MS) image and a panchromatic (PAN) image. In this paper, a new optimisation model is proposed for pan-sharpening. The proposed model consists of three terms: (i) a data synthesis fidelity term formulated on inferring the relationship between source MS image and fused image to preserve the spectral information, (ii) a total generalised variation-based prior term to inject the significant spatial details from PAN image to pan-sharpened image, and (iii) a spectral distortion reduction term that exploits the correlation between multispectral image bands. To solve the resultant convex optimisation problem, an efficient and convergence guaranteed operator splitting framework based on the alternating direction method of multipliers (ADMM) algorithm is formulated. Finally, the proposed model is experimentally validated using full-resolution and reduced-resolution data. The pan-sharpened outcomes exhibit the potential of the proposed method in enhancing the spatial and spectral quality. © 2020 Informa UK Limited, trading as Taylor & Francis Group.Item A performance study of an inter-UAV-based free space optical (FSO) system in the maritime environment(Walter de Gruyter GmbH, 2025) Nallagonda, V.R.; Nagaraju, N.S.; Bhavani, D.; Gogineni, R.; Suneel, S.; Krishnan, P.Recent advances in optical communication technology have enabled users to access high-data services on maritime links using the unmanned aerial vehicles (UAVs)-based free space optical (FSO) system. However, ensuring reliable data communication for maritime links is a significant challenge due to harsh weather conditions. To address the growing demand for maritime links, we have proposed a maritime UAV-based FSO communication system that can support high-speed data rates and provide extended communication coverage. This paper investigates various aspects of maritime UAV-based FSO systems, including the basic architecture, different channel characteristics, and application scenarios. The paper also includes an analysis of the outage probability of the Inter UAV-based FSO communication system, considering the lognormal distributed channel model for the maritime Inter UAV-based FSO link with heterodyne detection (hd). Additionally, it presents an accurate analytical channel model between UAVs. It compares analytical and simulation results, considering maritime turbulence, pointing error, and hovering UAV fluctuations. © 2025 Walter de Gruyter GmbH. All rights reserved.