In this paper we proposed fast and accurate interpolation and resizing of images using lifting scheme approach. 5/3 lifting scheme is an accurate and computationally inexpensive interpolation technique for image resizing. We compared the bilinear interpolation, Haar lifting scheme and 5/3 lifting scheme in this paper. The lifting scheme algorithm is applied for image interpolation to resize the image. In case of reduction in size, the image components are reduced and the reconstruction will be carried out to the original image. The reconstruction results are better by using Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) with other techniques like bilinear interpolation and Haar lifting schemes. The interpolation and reconstruction is executed in much less time with better MSE and PSNR as compared to Bilinear and Haar lifting schemes.
5/3 Lifting Scheme Approach for Image InterpolationIOSRJECE
In this paper we proposed fast and accurate interpolation and resizing of images using lifting scheme approach. 5/3 lifting scheme is an accurate and computationally inexpensive interpolation technique for image resizing. We compared the bilinear interpolation, Haar lifting scheme and 5/3 lifting scheme in this paper. The lifting scheme algorithm is applied for image interpolation to resize the image. In case of reduction in size, the image components are reduced and the reconstruction will be carried out to the original image. The reconstruction results are better by using Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) with other techniques like bilinear interpolation and Haar lifting schemes. The interpolation and reconstruction is executed in much less time with better MSE and PSNR as compared to Bilinear and Haar lifting schemes.
A Novel Super Resolution Algorithm Using Interpolation and LWT Based Denoisin...CSCJournals
Image capturing technique has some limitations and due to that we often get low resolution(LR) images. Super Resolution(SR) is a process by which we can generate High Resolution(HR) image from one or more LR images. Here we have proposed one SR algorithm which take three shifted and noisy LR images and generate HR image using Lifting Wavelet Transform(LWT) based denoising method and Directional Filtering and Data Fusion based Edge-Guided Interpolation Algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Satellite Image Resolution Enhancement Technique Using DWT and IWTEditor IJCATR
Now a days satellite images are widely used In many applications such as astronomy and
geographical information systems and geosciences studies .In this paper, We propose a new satellite image
resolution enhancement technique which generates sharper high resolution image .Based on the high
frequency sub-bands obtained from the dwt and iwt. We are not considering the LL sub-band here. In this
resolution-enhancement technique using interpolated DWT and IWT high-frequency sub band images and the
input low-resolution image. Inverse DWT (IDWT) has been applied to combine all these images to generate
the final resolution-enhanced image. The proposed technique has been tested on satellite bench mark images.
The quantitative (peak signal to noise ratio and mean square error) and visual results show the superiority of
the proposed technique over the conventional method and standard image enhancement technique WZP.
Wavelet-Based Warping Technique for Mobile Devicescsandit
The document proposes a wavelet-based warping technique to render novel views of compressed images on mobile devices. It uses Haar wavelet transform to compress large reference and depth images, reducing their size. The technique decomposes the images into approximation and detail parts, but only uses the approximation parts for warping. This improves rendering speed on mobile devices. The framework is implemented using Android tools and experiments show it provides faster rendering times for large images compared to direct warping without compression.
Change Detection from Remotely Sensed Images Based on Stationary Wavelet Tran...IJECEIAES
The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K ). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Interpolation Technique using Non Linear Partial Differential Equation with E...CSCJournals
This document presents a new image zooming algorithm that combines edge directed bicubic interpolation and a non-linear partial differential equation (PDE) method. The algorithm first uses edge directed bicubic interpolation to enlarge the image and fill empty pixels, producing a high resolution image. This noisy image is then input to a fourth-order PDE model for noise removal. Simulation results on test images show the proposed method achieves higher peak signal-to-noise ratios and structural similarity indices than other interpolation methods like bilinear and locally adaptive zooming. The method reduces artifacts and blurring near edges in zoomed images.
5/3 Lifting Scheme Approach for Image InterpolationIOSRJECE
In this paper we proposed fast and accurate interpolation and resizing of images using lifting scheme approach. 5/3 lifting scheme is an accurate and computationally inexpensive interpolation technique for image resizing. We compared the bilinear interpolation, Haar lifting scheme and 5/3 lifting scheme in this paper. The lifting scheme algorithm is applied for image interpolation to resize the image. In case of reduction in size, the image components are reduced and the reconstruction will be carried out to the original image. The reconstruction results are better by using Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) with other techniques like bilinear interpolation and Haar lifting schemes. The interpolation and reconstruction is executed in much less time with better MSE and PSNR as compared to Bilinear and Haar lifting schemes.
A Novel Super Resolution Algorithm Using Interpolation and LWT Based Denoisin...CSCJournals
Image capturing technique has some limitations and due to that we often get low resolution(LR) images. Super Resolution(SR) is a process by which we can generate High Resolution(HR) image from one or more LR images. Here we have proposed one SR algorithm which take three shifted and noisy LR images and generate HR image using Lifting Wavelet Transform(LWT) based denoising method and Directional Filtering and Data Fusion based Edge-Guided Interpolation Algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Satellite Image Resolution Enhancement Technique Using DWT and IWTEditor IJCATR
Now a days satellite images are widely used In many applications such as astronomy and
geographical information systems and geosciences studies .In this paper, We propose a new satellite image
resolution enhancement technique which generates sharper high resolution image .Based on the high
frequency sub-bands obtained from the dwt and iwt. We are not considering the LL sub-band here. In this
resolution-enhancement technique using interpolated DWT and IWT high-frequency sub band images and the
input low-resolution image. Inverse DWT (IDWT) has been applied to combine all these images to generate
the final resolution-enhanced image. The proposed technique has been tested on satellite bench mark images.
The quantitative (peak signal to noise ratio and mean square error) and visual results show the superiority of
the proposed technique over the conventional method and standard image enhancement technique WZP.
Wavelet-Based Warping Technique for Mobile Devicescsandit
The document proposes a wavelet-based warping technique to render novel views of compressed images on mobile devices. It uses Haar wavelet transform to compress large reference and depth images, reducing their size. The technique decomposes the images into approximation and detail parts, but only uses the approximation parts for warping. This improves rendering speed on mobile devices. The framework is implemented using Android tools and experiments show it provides faster rendering times for large images compared to direct warping without compression.
Change Detection from Remotely Sensed Images Based on Stationary Wavelet Tran...IJECEIAES
The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K ). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Interpolation Technique using Non Linear Partial Differential Equation with E...CSCJournals
This document presents a new image zooming algorithm that combines edge directed bicubic interpolation and a non-linear partial differential equation (PDE) method. The algorithm first uses edge directed bicubic interpolation to enlarge the image and fill empty pixels, producing a high resolution image. This noisy image is then input to a fourth-order PDE model for noise removal. Simulation results on test images show the proposed method achieves higher peak signal-to-noise ratios and structural similarity indices than other interpolation methods like bilinear and locally adaptive zooming. The method reduces artifacts and blurring near edges in zoomed images.
A lossless color image compression using an improved reversible color transfo...eSAT Journals
Abstract In case of the conventional lossless color image compression methods, the pixels are interleaved from each color component, and they are predicted and finally encoded. In this paper, we propose a lossless color image compression method using hierarchical prediction of chrominance channel pixels and encoded with modified Huffman coding. An input image is chosen and the R, G and B color channel is transform into YCuCv color space using an improved reversible color transform. After that a conventional lossless image coder like CALIC is used to compress the luminance channel Y. The chrominance channel Cu and Cv are encoded with hierarchical decomposition and directional prediction. The effective context modeling for prediction residual is adopted finally. It is seen from the experimental result the proposed method improves the compression performance than the existing method. Keywords: Lossless color image compression, hierarchical prediction, reversible color transform, modified Huffman coding.
Performance analysis of high resolution images using interpolation techniques...sipij
This paper presents various types of interpolation techniques to obtain a high quality image The difference
between the proposed algorithm and conventional algorithms (in estimation of missing pixel value) is that
if standard deviation of image is used to calculate pixel value rather than the value of nearmost neighbor,
the image gives the better result. The proposed method demonstrated higher performances in terms of
PSNR and SSIM when compared to the conventional interpolation algorithms mentioned.
A New Approach of Medical Image Fusion using Discrete Wavelet TransformIDES Editor
MRI-PET medical image fusion has important
clinical significance. Medical image fusion is the important
step after registration, which is an integrative display method
of two images. The PET image shows the brain function with
a low spatial resolution, MRI image shows the brain tissue
anatomy and contains no functional information. Hence, a
perfect fused image should contains both functional
information and more spatial characteristics with no spatial
& color distortion. The DWT coefficients of MRI-PET
intensity values are fused based on the even degree method
and cross correlation method The performance of proposed
image fusion scheme is evaluated with PSNR and RMSE and
its also compared with the existing techniques.
Comparative analysis of multimodal medical image fusion using pca and wavelet...IJLT EMAS
nowadays, there are a lot of medical images and their
numbers are increasing day by day. These medical images are
stored in large database. To minimize the redundancy and
optimize the storage capacity of images, medical image fusion is
used. The main aim of medical image fusion is to combine
complementary information from multiple imaging modalities
(Eg: CT, MRI, PET etc.) of the same scene. After performing
image fusion, the resultant image is more informative and
suitable for patient diagnosis. There are some fusion techniques
which are described in this paper to obtain fused image. This
paper presents two approaches to image fusion, namely Spatial
Fusion and Transform Fusion. This paper describes Techniques
such as Principal Component Analysis which is spatial domain
technique and Discrete Wavelet Transform, Stationary Wavelet
Transform which are Transform domain techniques.
Performance metrics are implemented to evaluate the
performance of image fusion algorithm. An experimental result
shows that image fusion method based on Stationary Wavelet
Transform is better than Principal Component Analysis and
Discrete Wavelet Transform.
Fast Stereo Images Compression Method based on Wavelet Transform and Two dime...BRNSSPublicationHubI
This document proposes a fast stereo image compression method using discrete wavelet transform and two dimensional logarithmic (TDL) algorithm for motion estimation. It first applies discrete wavelet transform to the stereo images to reduce computation time. It then estimates the disparity between the images using TDL algorithm to find the motion vectors. It encodes the motion vectors with Huffman encoding while compressing the remaining image as a still image. Experimental results on test stereo image pairs show that the proposed method produces good results in terms of PSNR, compression ratio, and computation time compared to other motion estimation algorithms.
Contrast enhancement using various statistical operations and neighborhood pr...sipij
This document proposes a novel contrast enhancement algorithm using various statistical operations and neighborhood processing. It begins with an overview of histogram equalization and some of its limitations. It then discusses related work on other histogram equalization techniques including classical histogram equalization, brightness preserving bi-histogram equalization, recursive mean separate histogram equalization, and background brightness preserving histogram equalization. The proposed method is then described, which applies statistical operations like mean and standard deviation within a neighborhood to locally enhance pixels. Pixels are replaced from an initially equalized image if their difference from the local mean exceeds a threshold. This aims to preserve local brightness features. Finally, metrics for evaluating image quality like PSNR, SSIM, and CNR are defined to analyze results
Image fusion can be defined as the process by which several images or some of their features
are combined together to form a fused image. Its aim is to combine maximum information
from multiple images of the same scene such that the obtained new image is more suitable for
human visual and machine perception or further image processing and analysis tasks. The
fusion of images acquired from dissimilar modalities or instrument has been successfully used
for remote sensing images. The biomedical image fusion plays an important role in analysis
towards clinical application which can support more accurate information for physician to
diagnose different diseases.
Wavelet Transform based Medical Image Fusion With different fusion methodsIJERA Editor
This paper proposes wavelet transform based image fusion algorithm, after studying the principles and characteristics of the discrete wavelet transform. Medical image fusion used to derive useful information from multimodality medical images. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide more information to the doctor and clinical treatment planning system. This paper based on the wavelet transformation to fused the medical images. The wavelet based fusion algorithms used on medical images CT and MRI, This involve the fusion with MIN , MAX, MEAN method. Also the result is obtained. With more available multimodality medical images in clinical applications, the idea of combining images from different modalities become very important and medical image fusion has emerged as a new promising research field
Weighted Performance comparison of DWT and LWT with PCA for Face Image Retrie...cscpconf
This paper compares the performance of face image retrieval system based on discrete wavelet
transforms and Lifting wavelet transforms with principal component analysis (PCA). These
techniques are implemented and their performances are investigated using frontal facial images
from the ORL database. The Discrete Wavelet Transform is effective in representing image
features and is suitable in Face image retrieval, it still encounters problems especially in
implementation; e.g. Floating point operation and decomposition speed. We use the advantages
of lifting scheme, a spatial approach for constructing wavelet filters, which provides feasible
alternative for problems facing its classical counterpart. Lifting scheme has such intriguing
properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image
retrieval. Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives
less computation and DWT-PCA gives high retrieval rate.. Especially ‘sym2’ wavelet
outperforms well comparing to all other wavelets.
ABSTRACT : Image registration is an important and fundamental task in image processing used to match two different images. Image registration estimates the parameters of the geometrical transformation model that maps the sensed images back to its reference image. A Feature-Based Approach to automated image-to-image registration is presented. In this paper, various methods are used in different Phases of Image registration. The characteristics of this approach is it combines scale interaction of Discrete wavelets for feature extraction, Scale Invariant Feature Transform (SIFT) for feature matching. Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. SIFT feature descriptor is invariant to uniform scaling, orientation, and partially invariant to affine distortion and illumination changes.
This document describes a content-based image retrieval system that uses 2-D discrete wavelet transform with texture features. It proposes using DWT to reduce image dimensions before extracting texture features from images using gray level co-occurrence matrix. Texture features and Euclidean distance are then used to retrieve similar images from a database. The system is tested on a dataset of 1000 images from 10 classes and achieves an average retrieval accuracy of 89.8%.
Content Based Image Retrieval Using 2-D Discrete Wavelet TransformIOSR Journals
This document proposes a content-based image retrieval system using 2D discrete wavelet transform and texture features. The system decomposes images using 2D DWT, extracts texture features from low frequency coefficients using GLCM, and retrieves similar images by calculating Euclidean distances between feature vectors. Experimental results on Wang's database show the proposed approach achieves 89.8% average retrieval accuracy.
Neural network based image compression with lifting scheme and rlceSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Neural network based image compression with lifting scheme and rlceSAT Journals
This document summarizes a research paper on neural network based image compression using wavelet transforms and lifting schemes. It discusses how wavelet transforms decompose images into frequency bands and how lifting schemes can reduce computational complexity. It also describes multilayer feedforward neural networks and how they can be used for image compression. Specifically, it proposes using a neural network with 64 input nodes, 8 hidden nodes, and 64 output nodes to compress 8x8 pixel blocks of an image by encoding the hidden node weights for transmission instead of the full image pixels. Simulation results showed this approach achieved better compression ratios and image quality than other existing methods.
E FFECTIVE P ROCESSING A ND A NALYSIS OF R ADIOTHERAPY I MAGESsipij
a-Si Electronic Portal Imaging Device (EPID) is an
important tool to verify the location of the radiat
ion
therapy beam with respect to the patient anatomy. B
ut, Electronic Portal Images (EPI) suffer from low
contrast. In order to have better in-treatment imag
es to extract relevant features of the anatomy, ima
ge
processing tools need to be integrated in the Radio
logy systems. The goal of this research work is to
inspect
several image processing techniques for contrast en
hancement of electronic portal images and gauge
parameters like mean, variance, standard deviation,
MSE, RMSE, entropy, PSNR, AMBE, normalised cross
correlation, average difference, structural content
(SC), maximum difference and normalised absolute
error (NAE) to study their visual quality improvem
ent. In addition, by adding salt and pepper noise,
Gaussian noise and motion blur, we calculate error
measurement parameters like Universal Image Quality
(UIQ) index, Enhancement Measurement Error (EME), P
earson Correlation Coefficient, SNR and Mean
Absolute error (MAE). The improved results point ou
t that image processing tools need to be incorporat
ed
into radiology for accurate delivery of dose
IRJET- Image Fusion using Lifting Wavelet Transform with Neural Networks for ...IRJET Journal
1) The document proposes using lifting wavelet transform and neural networks for medical image fusion to improve tumor detection. It aims to fuse CT and MRI images to provide more precise clinical information than either image alone.
2) Lifting wavelet transform is used to decompose the images into wavelet coefficients. A neural network then fuses the coefficients. This combined approach utilizes redundant information from the images while maintaining edge details.
3) Experimental results showed the proposed fusion technique produced discriminatory information suitable for human vision by efficiently combining features from the CT and MRI images.
Single image super resolution with improved wavelet interpolation and iterati...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
discrete wavelet transform based satellite image resolution enhancement muniswamy Paluru
The document discusses an image resolution enhancement technique using the discrete wavelet transform (DWT). It proposes interpolating the high-frequency subband images obtained from the DWT of the low-resolution input image. An intermediate stage is used to estimate the high-frequency subbands by utilizing the difference between the input image and its interpolated low-low (LL) subband. Inverse DWT is then applied to combine the interpolated images, generating the final enhanced image. The technique is compared to standard interpolation, wavelet zero padding, and state-of-the-art methods through qualitative and quantitative results. Matlab is identified as the required software for implementing the discrete wavelet transform and performing the proposed resolution enhancement algorithm.
IRJET- Wavelet Transform along with SPIHT Algorithm used for Image Compre...IRJET Journal
This document presents a method for medical image compression using lifting-based wavelet transform coupled with the SPIHT (Set Partition in Hierarchical Trees) algorithm. The lifting-based wavelet transform is used to decompose images into wavelet coefficients, addressing drawbacks of conventional wavelet transforms. SPIHT is then used to encode the wavelet coefficients. Experimental results showed the proposed method provides better PSNR, quality factor, and MSSIM values for medical images at low bit rates, compared to traditional compression methods. The compressed images can be represented in formats like GIF, JPG, BMP and PNG.
Image resolution enhancement by using wavelet transform 2IAEME Publication
This document discusses techniques for enhancing the resolution of digital images using wavelet transforms. It proposes a method that uses both stationary wavelet transform (SWT) and discrete wavelet transform (DWT) to decompose an input image into subbands, which are then interpolated using Lanczos interpolation before being combined via inverse DWT. The method is shown to achieve higher peak signal-to-noise ratios than traditional interpolation techniques like bilinear and bicubic interpolation as well as other wavelet-based super resolution methods, demonstrating its effectiveness for image resolution enhancement.
A lossless color image compression using an improved reversible color transfo...eSAT Journals
Abstract In case of the conventional lossless color image compression methods, the pixels are interleaved from each color component, and they are predicted and finally encoded. In this paper, we propose a lossless color image compression method using hierarchical prediction of chrominance channel pixels and encoded with modified Huffman coding. An input image is chosen and the R, G and B color channel is transform into YCuCv color space using an improved reversible color transform. After that a conventional lossless image coder like CALIC is used to compress the luminance channel Y. The chrominance channel Cu and Cv are encoded with hierarchical decomposition and directional prediction. The effective context modeling for prediction residual is adopted finally. It is seen from the experimental result the proposed method improves the compression performance than the existing method. Keywords: Lossless color image compression, hierarchical prediction, reversible color transform, modified Huffman coding.
Performance analysis of high resolution images using interpolation techniques...sipij
This paper presents various types of interpolation techniques to obtain a high quality image The difference
between the proposed algorithm and conventional algorithms (in estimation of missing pixel value) is that
if standard deviation of image is used to calculate pixel value rather than the value of nearmost neighbor,
the image gives the better result. The proposed method demonstrated higher performances in terms of
PSNR and SSIM when compared to the conventional interpolation algorithms mentioned.
A New Approach of Medical Image Fusion using Discrete Wavelet TransformIDES Editor
MRI-PET medical image fusion has important
clinical significance. Medical image fusion is the important
step after registration, which is an integrative display method
of two images. The PET image shows the brain function with
a low spatial resolution, MRI image shows the brain tissue
anatomy and contains no functional information. Hence, a
perfect fused image should contains both functional
information and more spatial characteristics with no spatial
& color distortion. The DWT coefficients of MRI-PET
intensity values are fused based on the even degree method
and cross correlation method The performance of proposed
image fusion scheme is evaluated with PSNR and RMSE and
its also compared with the existing techniques.
Comparative analysis of multimodal medical image fusion using pca and wavelet...IJLT EMAS
nowadays, there are a lot of medical images and their
numbers are increasing day by day. These medical images are
stored in large database. To minimize the redundancy and
optimize the storage capacity of images, medical image fusion is
used. The main aim of medical image fusion is to combine
complementary information from multiple imaging modalities
(Eg: CT, MRI, PET etc.) of the same scene. After performing
image fusion, the resultant image is more informative and
suitable for patient diagnosis. There are some fusion techniques
which are described in this paper to obtain fused image. This
paper presents two approaches to image fusion, namely Spatial
Fusion and Transform Fusion. This paper describes Techniques
such as Principal Component Analysis which is spatial domain
technique and Discrete Wavelet Transform, Stationary Wavelet
Transform which are Transform domain techniques.
Performance metrics are implemented to evaluate the
performance of image fusion algorithm. An experimental result
shows that image fusion method based on Stationary Wavelet
Transform is better than Principal Component Analysis and
Discrete Wavelet Transform.
Fast Stereo Images Compression Method based on Wavelet Transform and Two dime...BRNSSPublicationHubI
This document proposes a fast stereo image compression method using discrete wavelet transform and two dimensional logarithmic (TDL) algorithm for motion estimation. It first applies discrete wavelet transform to the stereo images to reduce computation time. It then estimates the disparity between the images using TDL algorithm to find the motion vectors. It encodes the motion vectors with Huffman encoding while compressing the remaining image as a still image. Experimental results on test stereo image pairs show that the proposed method produces good results in terms of PSNR, compression ratio, and computation time compared to other motion estimation algorithms.
Contrast enhancement using various statistical operations and neighborhood pr...sipij
This document proposes a novel contrast enhancement algorithm using various statistical operations and neighborhood processing. It begins with an overview of histogram equalization and some of its limitations. It then discusses related work on other histogram equalization techniques including classical histogram equalization, brightness preserving bi-histogram equalization, recursive mean separate histogram equalization, and background brightness preserving histogram equalization. The proposed method is then described, which applies statistical operations like mean and standard deviation within a neighborhood to locally enhance pixels. Pixels are replaced from an initially equalized image if their difference from the local mean exceeds a threshold. This aims to preserve local brightness features. Finally, metrics for evaluating image quality like PSNR, SSIM, and CNR are defined to analyze results
Image fusion can be defined as the process by which several images or some of their features
are combined together to form a fused image. Its aim is to combine maximum information
from multiple images of the same scene such that the obtained new image is more suitable for
human visual and machine perception or further image processing and analysis tasks. The
fusion of images acquired from dissimilar modalities or instrument has been successfully used
for remote sensing images. The biomedical image fusion plays an important role in analysis
towards clinical application which can support more accurate information for physician to
diagnose different diseases.
Wavelet Transform based Medical Image Fusion With different fusion methodsIJERA Editor
This paper proposes wavelet transform based image fusion algorithm, after studying the principles and characteristics of the discrete wavelet transform. Medical image fusion used to derive useful information from multimodality medical images. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide more information to the doctor and clinical treatment planning system. This paper based on the wavelet transformation to fused the medical images. The wavelet based fusion algorithms used on medical images CT and MRI, This involve the fusion with MIN , MAX, MEAN method. Also the result is obtained. With more available multimodality medical images in clinical applications, the idea of combining images from different modalities become very important and medical image fusion has emerged as a new promising research field
Weighted Performance comparison of DWT and LWT with PCA for Face Image Retrie...cscpconf
This paper compares the performance of face image retrieval system based on discrete wavelet
transforms and Lifting wavelet transforms with principal component analysis (PCA). These
techniques are implemented and their performances are investigated using frontal facial images
from the ORL database. The Discrete Wavelet Transform is effective in representing image
features and is suitable in Face image retrieval, it still encounters problems especially in
implementation; e.g. Floating point operation and decomposition speed. We use the advantages
of lifting scheme, a spatial approach for constructing wavelet filters, which provides feasible
alternative for problems facing its classical counterpart. Lifting scheme has such intriguing
properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image
retrieval. Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives
less computation and DWT-PCA gives high retrieval rate.. Especially ‘sym2’ wavelet
outperforms well comparing to all other wavelets.
ABSTRACT : Image registration is an important and fundamental task in image processing used to match two different images. Image registration estimates the parameters of the geometrical transformation model that maps the sensed images back to its reference image. A Feature-Based Approach to automated image-to-image registration is presented. In this paper, various methods are used in different Phases of Image registration. The characteristics of this approach is it combines scale interaction of Discrete wavelets for feature extraction, Scale Invariant Feature Transform (SIFT) for feature matching. Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. SIFT feature descriptor is invariant to uniform scaling, orientation, and partially invariant to affine distortion and illumination changes.
This document describes a content-based image retrieval system that uses 2-D discrete wavelet transform with texture features. It proposes using DWT to reduce image dimensions before extracting texture features from images using gray level co-occurrence matrix. Texture features and Euclidean distance are then used to retrieve similar images from a database. The system is tested on a dataset of 1000 images from 10 classes and achieves an average retrieval accuracy of 89.8%.
Content Based Image Retrieval Using 2-D Discrete Wavelet TransformIOSR Journals
This document proposes a content-based image retrieval system using 2D discrete wavelet transform and texture features. The system decomposes images using 2D DWT, extracts texture features from low frequency coefficients using GLCM, and retrieves similar images by calculating Euclidean distances between feature vectors. Experimental results on Wang's database show the proposed approach achieves 89.8% average retrieval accuracy.
Neural network based image compression with lifting scheme and rlceSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Neural network based image compression with lifting scheme and rlceSAT Journals
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Image Interpolation Using 5/3 Lifting Scheme Approach
1. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
DOI : 10.5121/sipij.2011.2106 71
Image Interpolation Using 5/3 Lifting Scheme
Approach
P.Ashok Babu1
and Dr.K.V.S.V.R Prasad2
1
Department of Electronics & Communication Engineering, NREC, Dhullapally,
Kompally, Hyderabad, A.P(state), INDIA.
ashokbabup2@gmail.com.
2
D.M.S.S.V.H. College of Engineering, Machilipatnam, Krishna (Dt), A.P(state),
INDIA.
KVSVR@YAHOO.COM.
Abstract
In this paper we proposed fast and accurate interpolation and resizing of images using lifting scheme
approach. 5/3 lifting scheme is an accurate and computationally inexpensive interpolation technique for
image resizing. We compared the bilinear interpolation, Haar lifting scheme and 5/3 lifting scheme in this
paper. The lifting scheme algorithm is applied for image interpolation to resize the image. In case of
reduction in size, the image components are reduced and the reconstruction will be carried out to the
original image. The reconstruction results are better by using Mean Squared Error (MSE) and Peak Signal
to Noise Ratio (PSNR) with other techniques like bilinear interpolation and Haar lifting schemes. The
interpolation and reconstruction is executed in much less time with better MSE and PSNR as compared to
Bilinear and Haar lifting schemes.
Keywords
Lifting scheme , 5/3 Lifting transform , DWT , IDWT , Image Resizing , Reconstruction.
1.INTRODUCTION
Wavelets provide accurate and excellent enough tool to approximate the functions, datasets and
signals. Mother wavelet acts as basic building block to approximate the complex signals and
functions at reasonable computational overhead. In other words, wavelets can approximate
complex signals by using a small set of coefficients without much computational complexity.
This is possible because, most data sets have correlation both frequency domain and in time (or
space) domain. Among the family of xxx let (wavelet, curvelet, ridgelet) transforms, wavelet is
the first one to preserve the correlation in both frequency domain and spatial domain.
Transformations in most of the frequency domain transforms do not preserve the original form of
a signal like image in spatial domain. One cannot make out the visible form of an image from the
results of cosine, sine or Discrete Fourier Transforms. However, the LPLP component of DWT
of an image seems to be similar to the original image and other components are the edges in
different directions. Thus DWT may serve as an excellent tool to simulate human vision
phenomenon like continuous interpolation, because of the time-frequency localization property of
wavelets.
2. DISCRETE WAVELET TRANSFORM (DWT)
When digital images are to be viewed or processed at multiple resolutions, the most popular
mathematical tool is discrete wavelet transform [1]. Wavelet transform is the first transform
which has been explored a lot in image and signal processing for its unique property of
maintaining the spatial domain and frequency domain contents. Wavelet series expansion maps a
2. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
72
function of continuous variables into a sequence of coefficients. Discrete wavelet transform
decomposes the input image (2-D signal) into four different wavelet coefficients at the first level.
The process of obtaining these four coefficients (Vetterli and Kovacevic, 1995) is presented in
Fig.1. After dividing the data set into two parts that is even and odd, average and difference is
computed. Out of these two coefficients, one is called the average and the other is the difference.
These two coefficients are again processed to compute average-average, average-difference,
difference-average and difference-difference terms. Thus after completion of the whole process
we have four coefficients. The coefficient average-average is also known as LPLP coefficient
(this is exactly visually similar to the original image but 50% in size), average-difference as
LPHP coefficient, difference-average as HPLP and
Fig. 1: QMF implementation of DWT
Even values Average
Odd values Difference
Fig. 2: Lifting scheme forward transform
difference-difference as HPHP. These four coefficients are exactly 50% of the original image
contain the different information of the original image as given below [2].
Split Predict Update
-
+
3. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
73
1).LPLP: This coefficient contains the low frequency image content and is the reduced resolution
2).LHLP: It contains Horizontal information of the image
3).HPLP: It contains vertical information of the image
4).HPHP: It contains diagonal information of the Image
3. LIFTING SCHEME
It is a technique to design wavelets and perform the discrete wavelet transform. The forward
lifting scheme wavelet transform divides the data set being processed into an even half and an
odd half [3].
3.1.Lifting scheme forward transform
Lifting scheme forward transform consists of three steps:
• Split
• Predict
• Update
3.1.1. Split: In split step the data is divided into ODD and EVEN elements.
3.1.2. Predict step: The difference between odd and even data forms the odd elements of the next
step wavelet transformation. The predict step, where the odd value of next iteration is “predicted”
from the even value of present step is described by (1). Index ‘j’ represents iteration and ‘i’
represents element:
Oddj+1,j= oddj,i+ P(evenj,i) (1)
Fig. 3: Lifting scheme inverse transform
3.1.3. Update step: The update step replaces the even elements of the next step with average of
the earlier step. These results in a smoother input (even element) for the next step wavelet
transform. The update step follows the predict phase. So in calculating average the update phase
must operate on the differences that are stored in the odd elements:
Evenj+1,i=eveni,j+U(oddj+1,i) (2)
A simple lifting scheme forward transform is as shown in Fig. 2.
Merge
Predict
Update
+
-
4. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
74
After dividing the complete data set into two parts that is even and odd, the processing is done as
follows: For the forward transform iteration j and element i, the new odd element j+1, i would be:
Oddj+1,i=oddj,j-evenj,i (3)
Even element of the next step is calculated as: Evenj+1,i=(evenj,i+oddj,i)/2 (4)
The original value of the odd j, i element has been replaced by the difference between this
element and its even predecessor. Simple algebra lets us recover the original value from Eq. 3:
Oddj,i=evenj,i+oddj+1,I (5)
Substituting this into the average that is Eq. 4
Evenj+1,i=(evenj,i+evenj,i+oddj+1,i)/2 (6)
Evenj+1, i=evenj,i+(oddj+1,i)/2 (7)
3.2.Lifting scheme inverse transform
As the name suggests the mirror process of forward lifting scheme transform. We recover the
original data sequence by going upwards as shown in Fig. 3. Additions are substituted for
subtractions and subtractions for additions. The merge step replaces the split step.
4. MATERIALS AND METHODS
4.1. Image resizing using lifting scheme
Image is a data set made of rows and columns. Monochrome images are two dimensional with
one intensity value associated with every pixel [4]. Being two dimensional, they are to be
processed first row wise and then column wise assuming that the image signal is orthogonal in
both the directions. Here, we present computation of one dimensional wavelet transform using
lifting scheme and then extend it to the 2D images to compute the Haar Wavelet transform.
Consider the following pixel row intensity data array:
S = {44, 20, 30, 48, 49, 39, 40, 35}
Step 1 (split): Initially this data set is divided into two parts. First, third, fifth, seventh pixel are
accommodated in the first part known as odd part. Second, forth, sixth, eighth pixel are
accommodated in the second part known as even part that is:
Odd = 44, 30, 49, 40 and Even = 20, 48, 39, 35
Now this even and odd data parts are processed in the following stepwise manner to obtain
average and difference terms.
Step 2 (predict): The first difference is obtained by subtracting the first even element from first
odd element. Similar procedure is adopted for remaining all elements of odd and even vector
sequentially, to result in even j+1,i and here DIFF1 vector sequence as given below:
evenj+1,i = oddj+1,i – evenj,i and thus
DIFF1= {44,-18,10,5}.
5. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
75
Step 3 (update): The first average term is obtained by adding the first odd and first even element
and by dividing this sum by two. This process is continued for the complete data set of odd and
even elements sequentially and the resultant vector oddj+1,i and AVG1 is obtained as shown
below:
Evenj+1,i = (evenj,i + oddj,i)/2 thus
AVG1 = {32, 39, 44, and 37.5}.
Thus initial eight elements present in a data array are reduced to four average and four difference
terms. Same procedure from Step 1-3 is repeated further, considering AVG1 vector as input data
array for the next level forward lifting scheme transformation. Thus the successive
transformations can be taken till each component reaches single coefficient.
For image processing applications, this procedure of Step 1-3 is applied to all the rows present in
the image. The resulting matrix is subjected to the same procedure of 1-3 Steps on all columns in
the image. This results in Low Pass Low Pass (LPLP) approximation of the image and is the 50%
reduced version of the original image. Thus at each level, the LPLP (AVG component after row-
wise and column-wise computation of lifting scheme DWT) is considered as the interpolated
version of the original image. The difference components obtained at every stage are used for
reconstruction of the original image. For the next level lifting scheme forward transformation
average component of earlier level acts as an input. At every lifting scheme forward
transformation, image size gets reduced to 1/2 of the input image size. Thus finally individual the
component (LPLP, LPHP, HPLP and HPHP) of an image gets reduced to single DWT coefficient
after successive transformations[5].
4.2. Reconstruction of the image using Inverse discrete wavelet transforms
The original data can be reconstructed with the help of inverse lifting scheme. Four pixels
obtained in forward lifting scheme transform now acts as an input to reconstruct average vector
in, inverse lifting scheme. Proceeding further in this manner as explained in materials and
methods till all average vectors (obtained in various levels of forward lifting scheme
transformation) gets processed, yields all the recovered elements in the last step. These
reconstructed elements correspond to the respective elements in the original array. Similar
procedure is adopted in row wise and column wise manner for the image. Image resizing with the
algorithm as presented in this paper is not possible if the image is not available in the size 2n
× 2n
.
The size of the original image with rows ‘r’ and columns ‘c’ is converted into the next (2n
× 2n
)
size by zero padding at the ends of all rows and columns i.e., adding (2n
-r) zeros in all the
columns and (2n
-c) zeros in all the rows at the ends.
5. 5/3 LIFTING TRANSFORM
A wavelet 5/3 lifting transform has 3 and 5 taps in the high and low pass analysis filters
respectively. 5/3 lifting transform is also known as Le Gall 5/3 transform. Le Gall 5/3
wavelet is the shortest symmetrical bi-orthogonal wavelet with two vanishing moments.
It is the simplest way to decompose the image into one high frequency component and
one low frequency component. The shortest bi orthogonal scaling and wavelet function
with two regularity factors at synthesis and analysis denoted (2,2) is attained with Le Gall
5/3 synthesis function. Interpolation of data means inserting additional data points into
the sequence to increase the sampling rate.
6. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
76
Let assume the pixels x1, x2, x3, x4, x5, x6 ... and in general we will predict even pixels
and update the odd pixels. The formulas used to predict and update even and odd pixels
using 5/3 lifting are
Pn=x4-((x3+x5)/2) (8)
Where Pn is the prediction of even ‘n’ pixels
Un=x3+((p2+p4)/4) (9)
Where Un is updation of odd pixels
5.1 5/3 Lifting Algorithm
1) Read the original image
2) For each scaling factor read the reference image and perform 5/3 lifting to predict and
update the image along horizontal and vertical directions.
3) For each down scaling level, down sample the update coefficients by a factor of two
and perform 5/3 lifting as above step.
4) For each up scaling perform nearest neighbour interpolation on pred samples and
apply inverse 5/3 lifting.
6. RESULTS AND DISCUSSION
From the results we can conclude that 5/3 lifting scheme will give more efficient results
compared to Bilinear and Haar lifting schemes. Fig.5 and Fig.7 are the comparisons of
Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Fig.4 and Fig.6 are
the resizing images using 5/3 lifting with different resize factors for Lena and Zelda. The
proposed algorithm is mainly focused on MSE and PSNR values. If we compare the three
algorithm the 5/3 lifting scheme will give the better results compared to bilinear and Haar
lifting schemes.
From the table 1 we compared the MSE and PSNR values for Lena image with different
resizing factors. From the table 2 we compared the MSE and PSNR values for Zelda
image. From the two tables we can observe that the 5/3 lifting scheme will gives the
better PSNR values and low MSE values than the Haar lifting scheme and bilinear
interpolation. If we increase the resize factor for an image the PSNR value increases and
the MSE value decreases. If we consider large resize factor the time taken by the image
to resize increases and the efficiency of the algorithm may decreases. The lifting scheme
allows a faster implementation of the wavelet transform. Traditionally, the fast wavelet
transform is calculated with a two-band sub band transform scheme, in Fig. 1. In each
step the signal is split into a high pass and low pass band and then sub sampled.
Recursion occurs on the low pass band. The lifting scheme makes optimal use of
similarities between the high and low pass filters to speed up the calculation.
7. CONCLUSION
In this particular paper we used 2n
× 2n
images for resizing. The proposed algorithm will
resize the image if the image is of the size 2n
× 2n
and zero padding enables application of
the scheme on all sizes of images but with additional computational overhead. If we use
7. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
77
the large images for resizing it will take more time to resize . So we can concentrate on
these large images as a future work to resize the images and to increase the more
efficiency.
Table1:Comparision of MSE and PSNR values for Lena Image
Bilinear
Interpolation
Haar Lifting 5/3 Lifting
Image Size
Resize
Factor
MSE PSNR MSE PSNR MSE PSNR
Lena
128×
128
0.25 866.8743 18.7512 492.0217 21.2110 332.8713 22.9080
Lena
256×
256
0.5 520.6248 20.9656 164.7891 25.9615 87.7668 28.6975
Lena
1024×
1024
2 188.9562 25.3672 82.6570 28.9580 38.8632 32.2354
Lena
2048×
2048
4 159.0445 26.1156 124.2400 27.1882 77.5673 29.2340
Table2:Comparision of MSE and PSNR values for Zelda Image
Bilinear
Interpolation
Haar Lifting 5/3 Lifting
Image Size
Resize
Factor
MSE PSNR MSE PSNR MSE PSNR
Zelda
128×
128
0.25 409.8525 22.0045 176.6397 25.6599 96.9163 28.2668
Zelda
256×
256
0.5 210.2132 24.9042 46.1388 31.4901 21.8254 34.7412
Zelda
1024×
1024
2 87.2603 28.7226 26.9042 33.8326 12.7555 37.0738
Zelda
2048×
2048
4 74.9172 29.3850 41.3637 31.9646 25.5541 34.0562
4(a) 4.(b
4. (c) 4. (d)
Fig.4: 5/3 Lifting based Image Resizing with resizing factors of (a) : 0.25 , (b) :0.5,
(c) : 2, (d) : 4, For Lena
8. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
78
. 5(a)
5(b)
Fig. 5: (a) Comparison of MSE and (b) Comparison of PSNR for Lena Image.
6. (a) 6. (b)
6.(c) 6.(d)
Fig.6: 5/3 Lifting based Image Resizing with resizing factors of (a) : 0.25 , (b) :0.5,
(c) : 2, (d) : 4 for Zelda
9. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
79
7(a)
7(b)
Fig. 7: (a) Comparison of MSE and (b) Comparison of PSNR for Zelda Image.
10. Signal & Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
80
8.REFERENCES
[1]. Asamwar, R.S., K.M. Bhurchandi and A.S.Gandhi, 2009. “Piecewise lifting scheme based DWT to
model human vision interpolation phenomenon”. Proceeding of the IAENG International Conference
on Signal and Image Engineering, July 1-3, IAENG, London, UK., pp: 822-827.
http://www.iaeng.org/publication/WCE2009/WCE 2009_pp822-827.pdf
[2]. Asamwar, R.S., K.M. Bhurchandi and A.S. Gandhi, 2010. “Interpolation of images using discrete
wavelet transform to simulate image resizing as in human vision”. Int. J. Autom. Comput., 7: 9-16.
DOI: 10.1007/S116333-010-0009-
[3]. Daubechies, I. And W. Sweldens,1998. “Factoring wavelet transforms into lifting steps”. J. Fourier
Anal. Appli., 49: 247-269. DOI: 10.1.1.106.8124
[4] Latha, Y.M., B.C. Jinaga and V.S.K. Reddy, 2007. “A precise content based image retrieval: Lifting
Scheme”. Int. J. Comput. Sci. Network Secur. 7: 38-45.
[5] Asamwar, R.S., K.M. Bhurchandi and A.S. Gandhi, 2010. “Successive Image Interpolation Using
Lifting Scheme” Approach. Journal of Computer Science 6 (9): 961-970
1
Mr.P.Ashok Babu obtained B.E Degree in 2001 from Andhra University, M.E
Communication Engineering) in 2005 Osmania University. He pursuing the Ph.D.
from JNTUH university, Hyderabad in Digital Image Processing . He published two
papers in International journals and one paper in International Conference. He is
member of IACSIT and IAENG . He worked in various positions in the Department
of ECE, GRIET, Hyderabad. Presently he is working as Assoc. Professor and Head,
Department of ECE, Narasimha Reddy Engineering College, Dhullapally,
Hyderabad, Andhra Pradesh (state) India.
2
Dr. K.V.S.V.R Prasad obtained B Sc. Degree in 1963 from Andhra University,
B.E (Telecommunication Engineering) in 1967 and M.E (ECE) Microwave
Engineering specialization in 1977 from Osmania University. He received the Ph.D.
in 1985 from IIT Kharagpur in strip and micro strip transmission lines. He published
six papers in IEEE Transactions in MTT, Antenna and Propagation and EMI/EMC
and three papers in National Conferences. He is fellow of IETE (life member). He
worked in various capacities in the Department of ECE, Osmania University,
Hyderabad. Presently he is working as professor and head, Department of ECE,
D.M.S.S.V.H. college of engineering, Machilipatnam, Andhra Pradesh (state) India.