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Arumuga Perumal, S.
- WSES: High Secured Data Encryption and Authentication Using Weaving, Rotation and Flipping
Authors
1 Department of Computer Science, S.T. Hindu College, IN
Source
ICTACT Journal on Communication Technology, Vol 6, No 4 (2015), Pagination: 1200-1207Abstract
Data security is the very important part in the network data communication. Avoidance of the information hacking and steeling are very challenging part for network data communication. Now-a-days people are using many encryption and decryption techniques for data security. But all encryption and decryption techniques are having more time occupation or less security for the process. This paper proposed high level security approach to encryption and decryption for data security. Two levels of securities are used in this proposed method. First one is data encryption and the second one is hash value generation. The proposed Weaving based Superior Encryption Standard (WSES) uses a novel weaving based approach. The weaving array generation is done by Elementary Number Theory Notation (ENTN) method. The weaving array has multiple private keys for XOR encryption. After encryption the error value is extracted from the encrypted array and weaving array. This error value is sent to the other side. The novel approach for hash value generation uses the encrypted array. After encryption, the encrypted array is rotated into four degrees and each degree data are converted to vector format and arranged on by one under the vector. Finally a 2D Rotational Encryption Matrix (REM) is obtained. After this process a REM copy is converted to mirror flip and it is need as Flipped Matrix (FM). The FM is concatenated under the REM and converted to vector using the zigzag operation. Finally this process gives two bytes hash value from the vector. This proposed method executes very fast and provide high security. This method is much reliable to small size applications and also used for any type of data security.Keywords
Data Security, XOR, WSES, Encryption, Decryption, Hash, ENTN, Weaving, REM, Flipped Matrix.
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- A Novel Approach to Image Denoising by Combining Neighshrink and Sureshrink in Wavelet Domain
Authors
1 Department of Computer Science, Sadakathullah Appa College, Tirunelveli-627011, Tamilnadu, IN
2 Department of Computer Science, S. T. Hindu College, Nagarcoil-629002, Tamilnadu, IN
3 Centre for Information Technology and Engineering, Manonmanium Sundaranar University, Tirunelveli, Tamilnadu-627012, IN
4 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar-626001, Tamilnadu, IN
Source
Digital Image Processing, Vol 2, No 2 (2010), Pagination: 60-67Abstract
Removing noise from the original image is still a challenging problem for researchers. A traditional way to remove noise from image data is to employ spatial filters. With wavelet transform gaining popularity in the last two decades, various algorithms for denoising in wavelet domain were introduced. In this paper, it is proposed to combine Neighshrink and Sureshrink to denoise an image corrupted by additive white Gaussian noise in wavelet domain.Keywords
Image Denoising, Dual Tree Discrete Wavelet Packet Transform, Root Mean Square Error, Peak Signal to Noise Ratio, Quality Index and Normalized Weighted Performance Metric.- Area Level Fusion of Multi-Focused Images Using Dual Tree Complex Wavelet Packet Transform
Authors
1 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar-626 001, Tamilnadu, IN
2 Department of Computer Science, St. Hindu Colleg, Nagarcoil–629 002, Tamilnadu, IN
3 Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar–626 001, Tamilnadu, IN
Source
Digital Image Processing, Vol 1, No 5 (2009), Pagination: 213-220Abstract
The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion,namely spatial fusion and multi scale transform fusion. In spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at thatlocation. Multi scale transform fusion uses transform for representing the source image at multi scale. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform (DWT) since it minimizes structural distortions. But, wavelet transform suffers due to poor directionality and does not provide a geometrically oriented decomposition in multiple directions. One way to generalize the discrete wavelet transform so as to generate a structured dictionary of base is given by the Discrete Wavelet Packet Transform (DWPT). This benefit comes from the ability of the wavelet packets to better represent high frequency content and high frequency oscillating signals in particular. However, it is well known that both DWT and DWPT are shift varying. The Dual Tree Complex Wavelet Transform (DTCWT) introduced by Kingsbury, is approximately shift -invariant and provides directional analysis. And there are three levels for image fusion namely pixel level, area level and region level. In this paper, it is proposed to implement area level fusion of multi focused images using Dual Tree Complex Wavele Packet Transform (DTCWPT), extending the DTCWT as the DWPT extends the DWT and the performance is measured in terms of various performance measures like ischolar_main mean square error, peak signal to noise ratio, quality index and normalized weighted performance metric.
Keywords
Image fusion, Dual Tree Discrete Wavelet Packet Transform, Root Mean Square Error, Peak Signal to Noise Ratio, Quality Index and Normalized Weighted Performance Metric.- Area level fusion of Multi-focused Images using Double Density DWT and DTCWT
Authors
1 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar–626 001, Tamilnadu, IN
2 Department of Computer Science, St. Hindu Colleg, Nagarcoil–629 002, Tamilnadu, IN
3 Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar–626 001, Tamilnadu, IN
Source
Digital Image Processing, Vol 1, No 6 (2009), Pagination: 231-242Abstract
Image fusion is a process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely spatial fusion and multi scale transform fusion. In spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Multi scale transform fusion usestransform for representing the source image at multi scale. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform (DWT) since it minimizes structural distortions. But, wavelet transform cannot provide efficient approximation for directional features of images which in turn affects the performance of DWT-based image fusion schemes. Many multi scale tools have been invented to boost image fusion performance by incorporating directional representation. These tools can be classified into two categories according to the domain where they are designed: Spatial-domain Multiscale Directional Transform (SMDT) and Frequency domain Multiscale Directional Transform (FMDT). In FMDT, the basis functions of each subband orient at a certain direction, overcoming the poor directionality of 2-D DWT. Representative work includes curvelets, contourlets, bandelets, directionlets, multiscale directional filter banks, and complex wavelets. The critically sampled DWT is not a shift-invariant discrete transform, but the Dual Tree Complex Wavelet Transform (DT-CWT) introduced by Kingsbury is approximately shift -invariant and provides directional analysis whereas the undecimated DWT (UDWT) is an exactly shift-invariant transform. When J scales are implemented, the UDWT is expansive by the factor J + 1. The Double-density Discrete Wavelet Transform (DDWT) proposed by Ivan W. Selesnick provides a compromise between the UDWT and the critically-sampled DWT. A Double-density DTCWT (DDT-CWT), also proposed by Ivan W. Selesnick is an over-complete DWT designed to simultaneously possess the good properties of the DDWT and the DTCWT. And there are three levels for image fusion amel pixel level, area level and region level. In this paper, it is proposed to implement area level fusion of multi focused images using Double Density DWT and DTCWPT and the performance is measured in terms of various performance measures like ischolar_main mean square error and peak signal to noise ratio.
Keywords
Image fusion, DDWT, DDT-CWT, Root Mean Square Error, Peak Signal to Noise Ratio.- A Statistical Sharpness Measure Based Multi Focus Image Fusion Using Double Density Discrete Wavelet Transform
Authors
1 Department of Mechanical Engineering, Kamaraj College of Engineering and Technology, IN
2 Department of Computer Science, S.T. Hindu College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 3, No 3 (2013), Pagination: 577-582Abstract
Image fusion is the process of combining two or more images of the same scene to form the fused image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. Transform fusion uses transform for representing the source image at multi scale. Due to the compactness, orthogonality and directional information, the Discrete Wavelet Transforms and its undecimated version are used for image fusion. These transforms can be implemented using perfect reconstruction Finite Impulse Response filter banks which are either symmetric or orthogonal. To design filters to have both symmetric and orthogonal properties, the number of filters is increased to generate M-band transform. Double density Discrete Wavelet Transform is an example of M-band DWT and consists of one scaling and two wavelet filters. In this paper, an approach for DDWT based image fusion is designed using statistical property of wavelet filters in representing the sharpness and its performance is measured in terms of Root Mean Square Error, Peak to Signal Noise Ratio, Quality Index.Keywords
Image Fusion, Discrete Wavelet Transform (DWT), Finite Impulse Response Filter, M-Band Transform and Double Density Discrete Wavelet Transform (DDWT).- Optimal Level of Decomposition of Stationary Wavelet Transform for Region Level Fusion of Multi-Focused Images
Authors
1 Kamaraj College of Engineering and Technology, Tamil Nadu, IN
2 S.T. Hindu College, Tamil Nadu, IN
Source
ICTACT Journal on Image and Video Processing, Vol 1, No 2 (2010), Pagination: 76-79Abstract
In machine vision, due to the limited depth-of-focus of optical lenses in CCD devices, it is not possible to have a single image that contains all the information of objects in the image. To achieve this, image fusion is required which is usually refers to the process of combining two or more different images, each containing different features into a new single image retaining important features from each and every image with extended information content. The approaches to image fusion can be classified into two namely Spatial Fusion and Transform fusion. The most commonly used transform for image fusion at multi scale is Discrete Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance and this disadvantage is overcome by Stationary Wavelet Transform. This paper describes the optimum level of decomposition of Stationary Wavelet Transform for region based fusion of multi focused images in terms of various performance measures.Keywords
Image Fusion, Region Level Fusion, Discrete Wavelet Transform and Stationary Wavelet Transform.- A Fuzzy Filtering Model for Contour Detection
Authors
1 Department of Computer Science, St. Xavier’s College, Tamil Nadu, IN
2 Department of Computer Science, S.T. Hindu College, Tamil Nadu, IN
3 Department of Information Technology, M.S. University, Tamil Nadu, IN
Source
ICTACT Journal on Soft Computing, Vol 1, No 4 (2011), Pagination: 197-200Abstract
Contour detection is the basic property of image processing. Fuzzy Filtering technique is proposed to generate thick edges in two dimensional gray images. Fuzzy logic is applied to extract value for an image and is used for object contour detection. Fuzzy based pixel selection can reduce the drawbacks of conventional methods(Prewitt, Robert). In the traditional methods, filter mask is used for all kinds of images. It may succeed in one kind of image but fail in another one. In this frame work the threshold parameter values are obtained from the fuzzy histogram of the input image. The Fuzzy inference method selects the complete information about the border of the object and the resultant image has less impulse noise and the contrast of the edge is increased. The extracted object contour is thicker than the existing methods. The performance of the algorithm is tested with Peak Signal Noise Ratio(PSNR) and Complex Wavelet Structural Similarity Metrics(CWSSIM).Keywords
Contour Detection, Threshold, Histogram, Fuzzy Filtering, Fuzzy Logic.- Fuzzy Based Contrast Stretching for Medical Image Enhancement
Authors
1 Department of Computer Science, St. Xavier’s College, Tamil Nadu, IN
2 Department of Computer Science, S.T. Hindu College, Tamil Nadu, IN
3 Department of Information Technology, Manonmaniam Sundaranar University, Tamil Nadu, IN