Search

Your search keyword '"Sudeep, P. V."' showing total 17 results
Start Over Author "Sudeep, P. V."
17 results on '"Sudeep, P. V."'

1. An Improved AttnGAN Model for Text-to-Image Synthesis

Author

Gopalakrishnan, Remya, Sambagni, Naveen, Sudeep, P. V., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kaur, Harkeerat, editor, Jakhetiya, Vinit, editor, Goyal, Puneet, editor, Khanna, Pritee, editor, Raman, Balasubramanian, editor, and Kumar, Sanjeev, editor

Published
2024
Full Text
View/download PDF

3. U-shaped Transformer for Enhancing Low-Dose CT Images

Author

Unnikrishnan, Aswin, Pavithran, Amal, Naik, Arpith G., Jiju, Abhishek P., Sudeep, P. V., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Harish, editor, Shrivastava, Vivek, editor, Kumari Bharti, Kusum, editor, and Wang, Lipo, editor

Published
2022
Full Text
View/download PDF

4. Blind Image Restoration with CNN Denoiser Prior

Author

Repala, Hari Kishan, Christopher, Aneeta, Sudeep, P. V., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saraswat, Mukesh, editor, Roy, Sarbani, editor, Chowdhury, Chandreyee, editor, and Gandomi, Amir H., editor

Published
2022
Full Text
View/download PDF

5. Image Denoising Using DnCNN: An Exploration Study

Author

Murali, Vineeth, Sudeep, P. V., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jayakumari, J., editor, Karagiannidis, George K., editor, Ma, Maode, editor, and Hossain, Syed Akhter, editor

Published
2020
Full Text
View/download PDF

13. A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps

Author

Sudeep, P. V. (author), Palanisamy, P. (author), Kesavadas, Chandrasekharan (author), Sijbers, JJM (author), den Dekker, A.J. (author), Rajan, Jeny (author), Sudeep, P. V. (author), Palanisamy, P. (author), Kesavadas, Chandrasekharan (author), Sijbers, JJM (author), den Dekker, A.J. (author), and Rajan, Jeny (author)

Abstract

A phase map can be obtained from the real and imaginary components of a complex valued magnetic resonance (MR) image. Many applications, such as MR phase velocity mapping and susceptibility mapping, make use of the information contained in the MR phase maps. Unfortunately, noise in the complex MR signal affects the measurement of parameters related to phase (e.g, the phase velocity). In this paper, we propose a nonlocal maximum likelihood (NLML) estimation method for enhancing phase maps. The proposed method estimates the true underlying phase map from a noisy MR phase map. Experiments on both simulated and real data sets indicate that the proposed NLML method has a better performance in terms of qualitative and quantitative evaluations when compared to state-of-the-art methods., Accepted Author Manuscript, Team Michel Verhaegen

Published
2017
Full Text
View/download PDF

16. A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans.

Author

Girish GN, Anima VA, Kothari AR, Sudeep PV, Roychowdhury S, and Rajan J

Subjects
Humans, Algorithms, Automation, Benchmarking, Cysts diagnostic imaging, Retinal Diseases diagnostic imaging, Tomography, Optical Coherence
Abstract

(background and Objectives): Retinal cysts are formed by accumulation of fluid in the retina caused by leakages from inflammation or vitreous fractures. Analysis of the retinal cystic spaces holds significance in detection and treatment of several ocular diseases like age-related macular degeneration, diabetic macular edema etc. Thus, segmentation of intra-retinal cysts and quantification of cystic spaces are vital for retinal pathology and severity detection. In the recent years, automated segmentation of intra-retinal cysts using optical coherence tomography B-scans has gained significant importance in the field of retinal image analysis. The objective of this paper is to compare different intra-retinal cyst segmentation algorithms for comparative analysis and benchmarking purposes., (methods): In this work, we employ a modular approach for standardizing the different segmentation algorithms. Further, we analyze the variations in automated cyst segmentation performances and method scalability across image acquisition systems by using the publicly available cyst segmentation challenge dataset (OPTIMA cyst segmentation challenge)., (results): Several key automated methods are comparatively analyzed using quantitative and qualitative experiments. Our analysis demonstrates the significance of variations in signal-to-noise ratio (SNR), retinal layer morphology and post-processing steps on the automated cyst segmentation processes., (conclusion): This benchmarking study provides insights towards the scalability of automated processes across vendor-specific imaging modalities to provide guidance for retinal pathology diagnostics and treatment processes., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Published
2018
Full Text
View/download PDF

17. Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering.

Author

Sudeep PV, Issac Niwas S, Palanisamy P, Rajan J, Xiaojun Y, Wang X, Luo Y, and Liu L

Subjects
Humans, Phantoms, Imaging, Signal-To-Noise Ratio, Tomography, Optical Coherence instrumentation, Image Processing, Computer-Assisted, Models, Theoretical, Tomography, Optical Coherence methods
Abstract

Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results