Work place: YV University, Kadapa, AP, India
Research Interests: Medical Image Computing, Image Processing, Image Manipulation, Image Compression, Medical Informatics
Dr. C. Naga Raju received his B.Tech degree in Computer Science from J.N.T.University Anantapur, M.Tech degree in Computer Science from J.N.T.University Hyderabad and PhD in digital Image processing from J.N.T.University Hyderabad. Currently, he is working as an Associate professor in YSR College of Engineering of YV University, Poddutur. He has 16 years of teaching experience. He has published 100+ research papers in various National and International Journals and about 50+ research papers in various National and International Conferences. He has attended twenty seminars and workshops.
He is member of various professional societies like IEEE, ISTE and CSI.
DOI: https://doi.org/10.5815/ijigsp.2018.03.05, Pub. Date: 8 Mar. 2018
Impulse noise is the prime factor which reduces the quality of the digital image and it erases the important details of the images. De-noising is an indispensable task to restore the image features from the corrupted low- quality images and improve the perceptual quality of images. Several techniques are used for image quality enhancement and image restoration. In this work, an image de-noising scheme is developed to detect and correct the impulse noise from the image by using fuzzy entropy. The proposed algorithm is designed in two phases, such as noise detection phase, and correction phase. In the noise detection phase, the fuzzy entropy of pixels in a window of interest (WoI) is computed to detect whether the pixel is noisy or not. The Fuzzy entropy of pixel greater than specified alpha cut value will be considered as noise pixel and submitted to correction phase. In the correction phase noise pixel value is replaced with a fuzzy weighted mean of the un-corrupted pixels in the WoI. The proposed Fuzzy entropy based impulse noise detection and correction method are implemented using MATLAB. The experimentation has been carried out on different standard images and the analysis is performed by comparing the performance of the proposed scheme with that of the existing methods such as DBA, MDBUTMF, AMF, NAFSM, BDND, and CM , using PSNR, SSIM, and NAE as metric parameters. The proposed method will give good results compared to state of the art methods in image restoration.[...] Read more.
DOI: https://doi.org/10.5815/ijcnis.2016.08.06, Pub. Date: 8 Aug. 2016
Researchers have already shown the way, how to improve and compare the existing MANET routing protocols that help us to understand the basic feature and functionality of the various routing protocols. However, while these routing protocols have been proposed from different research groups in the literature, which shows the existing routing protocols, are not consistent to common framework to evaluate its performance. Moreover, these protocols are vulnerable to many collaborative attacks, due to its cooperative nature of routing algorithms. Hence, it is difficult for one to choose a proper routing protocol for a given application; therefore, we initially study and review to compare the different existing routing protocols in adversarial environment with varying traffic and mobility simulation scenarios. This paper addresses the comparison of various reactive routing protocols in adversarial environment. To achieve this, we had investigated with widely used NS-2 simulators for fair comparisons of different routing protocols. Furthermore, we also develop a collaborative adversary model for these existing routing protocols that can interfere with communications to subvert the normal operation of the network. Specifically, Our extensive simulation results shows the relative quantitative analysis for comparing the performance of reactive routing protocols such as AODV, DSR under adversarial environments with varying traffic and mobility simulation scenarios. Moreover, the performance of these protocols is measured with the various metrics such as throughput, end-to-end delay, packet delivery ratio, and routing overhead.[...] Read more.
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