Double Transform Research Articles

Multilayered Secure Medical Image Transmission with High Payload Using Double Density Dual Tree Discrete Wavelet Transform

Multilayered Secure Medical Image Transmission with High Payload Using Double Density Dual Tree Discrete Wavelet Transform

An Efficient CRT based Digital Image Watermarking using Double Density Wavelet Transform

Digital Watermarking is an unpretentious and effective approach to afford copyright fortification. Watermark transparency is obligatory primarily for copyright protection. The challenge is to introduce a digital watermark that is both transparent and highly robust to common signal processing and possible attacks. The two basic requirements for an effective watermarking scheme, robustness and transparency, conflict with each other.This paper proposesa watermarking technique for digital gray scale images that is based on utilizing congruence’s in number theory and its generalizations in abstract algebra which have been developed in the context of image compression. Watermarking scheme is based on a Chinese Remainder Theorem along with Double Density Discrete Wavelet Transform framework individually which allows for the possibility of directly watermarking the Image bitstream, Watermark region selection is based on transformation level. In proposed scheme, initially, the cover image and watermark are transformed into spatial domain using Double Density Wavelet Transform and then singular values of these transformed images are combined using CRT coefficients. Our scheme is shown to provide very good results both in terms of image transparency and robustness. An optimal watermark embedding method is developed to achieve minimum watermarking distortion. Experimental results are provided in terms of Peak signal to noise ratio (PSNR), Mean Squared Error (MSE), Correlation and Weighted Peak signal to noise ratio (WPSNR) to demonstrate the effectiveness of the proposed algorithm.

Lossless Color Image Compression using Double Level RCT in BBWCA

A new lossless image compression scheme for natural color images which is based on double level Reversible Color Transform(RCT) and Bi-level Burrows Wheeler Compression Algorithm(BBWCA) is proposed. The first level RCT from RGB to HSV provide higher reduction in number of unique hue components, thus gives higher compression in case of natural images. The first level RCT followed by the second level RCT from HSV to YUV helps to yield small number of unique Y component values. DC level shifting and twos complement operation will be applied as preprocessing steps to second level RCT. The result of double level RCT is used as the input to BBWCA, in which row-wise BWT followed by column-wise BWT is applied. The compressed image data is formed by using move-to-front(MTF), Run-length-encoding and Entropy coding. The proposed method using double level RCT with BBWCA results in high compression by taking advantage of reduction in hue components of natural images. Among the different color space compared, the proposed method achieves better compression and is well suited for small and large size natural images. Proposed method make use of a double level RCT on the existing BBWCA algorithm and resulted in improving the compression ratio by 46 percentage.

A Two Stage Approach with ICA and Double Density Wavelet Transform for Artifacts Removal in Multichannel EEG Signals

Presence of artifacts in electroencephalographs (EEG) is major hurdles for the precise analysis of spectral behavior. For suppression of ocular artifact in EEG this paper proposes a component based Independent Component Analysis (ICA) model. It involves the generating a set of individual components of given signal followed by rejection of unwanted artifacts. Further this paper presents a novel method with combination of ICA, information sharing and double density wavelet transform to reject the artifacts from the signal. The Independent Component Analysis (ICA) here is used to segment artifact peaks in the signal. Then the Discrete Wavelet Transform is applied for multi-level transfer of signal data until the reception of significant result. The Wavelet ICA suppression not only removes artifacts but also preserves the spectral and coherence properties of brain signals.

Double Two-Dimensional Discrete Fast Fourier Transform for Determining of Geometrical Parameters of Fibers and Textiles

We have developed a computing method to determine the geometrical parameters of fibers and the textile periodical structure. This method combines two two-dimensional discrete fast Fourier transforms to calculate a diffraction pattern from a diffraction pattern image of material under consideration. The result is the same as that of observation of a diffraction pattern which is achieved by illuminating the diffraction pattern image of material by a beam of coherent monochromatic light. After the first transform we obtain the Fraunhofer diffraction picture with clearly visible elements of the periodical structure of material, but distances in this picture are reciprocally proportional to distances in the periodical structure of the source object so additional calculations are required. After the second transform we have a clear periodical structure of diffraction maximums where distances between them are equal to distances between repeating elements in the source material (fibers, knots, yarns, etc.).

A STATISTICAL SHARPNESS MEASURE BASED MULTI FOCUS IMAGE FUSION USING DOUBLE DENSITY DISCRETE WAVELET TRANSFORM

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.

A Hybrid Model for Secure Data Transfer in Audio Signals using HCNN and DD DWT

In today’s world, there are a number of cryptographic and steganography techniques used in order to have secured data transfer between a sender and a receiver. In this paper a new hybrid approach that integrates the merits of cryptography and audio steganography is presented. First, the original message is encrypted using chaotic neural network and the resultant cipher text is embedded into a cover audio using Double Density Discrete Wavelet Transform (DD DWT). The resultant stego audio is transmitted to the receiver and the reverse process is done in order to get back the original plain text. The proposed method presents a Steganography scheme along with the cryptography scheme which enhances the security of the algorithm.

Image Steganogaraphy Based on Complex Double Dual Tree Wavelet Transform

In this paper we present an improved steganographic algorithm based on the modified LSB method in four different wavelet transform domains, viz, Wavelet (wavcdf97), Slantlet, Double Density Dual Tree Discrete Wavelet transform and Double Density Dual Tree Complex Wavelet, respectively. The characteristics of the Complex Wavelet Transform that provides shift invariance and offers better directional selectivity makes it a better choice for data hiding over the classical discrete wavelet transform. For achieving the security and robustness, the original message is first converted into binary cell array with the help of self-synchronizing variable length codes, viz., T-codes before the embedding. The use of T-codes does not only compress the message but also provides self-synchronization at decoding stage. The secret message so obtained is embedded in the insensitive high sub-bands obtained from the cover image after applying these transform. The metric used for evaluating the visual quality of stego-image is PSNR. The experimental results show that the dual tree complex wavelet transform is better option than the Wavelet, Wavelet-like transform, i.e., Slantlet transform and Double Density Dual Tree DWT for data hiding in terms of visual quality and embedding capacity, though poor to external attacks such as Gaussian.

Image Reconstruction of Compressed Sensing Based on the Double Density Circular Symmetric Contourlet Transform and Bivariate Model

Compressed sensing uses the sparse prior of image representation,it can reconstruct image from samples much less than Nyquist rate.The sparse image representation and sparseness measure are two key ingredients which have important role on image reconstruction performance.To achieve the better sparse image representation,we split one octave scale of circular symmetric contourlet transform into two scales according to the characteristic of simple cell receptive field,and yield the double density circular symmetric contourlet transform(DDCSCT).The radial bandwidth ratio of the DDCSCT is 1.414.According to the characteristics of the DDCSCT joint distribution,we deduce the second order sparseness measure of image reconstruction from bivariate model.The experiment results show that proposed image reconstruction algorithm which combine DDCSCT and second order sparseness measure outperforms the classical algorithms in terms of both peak signal-to-noise ratio and visual quality.

EFFECTIVE MULTI-RESOLUTION TRANSFORM IDENTIFICATION FOR CHARACTERIZATION AND CLASSIFICATION OF TEXTURE GROUPS

ICTACT Journals are peer reviewed International Journals published quarterly. With the objective of creating Indian-based Journals of International quality and to build up local research content, ICTACT Journals exists for the purpose of providing a quality peer-reviewed platform for Scientists, Researchers, Academicians and Engineers who carry out their original research. The Journals will highlight the continued growth and new challenges in both basic research and application development.

Image Denoising Algorithm Based on the Nonsubsampled Double Density Contourlet Transform

Image Denoising Algorithm Based on the Nonsubsampled Double Density Contourlet Transform

The Review of Feature Level Fusion of Multi-Focused Images Using Wavelets

Abstract: 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 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. The most common widely 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 poor directional selectivity. These two disadvantages are overcome by Stationary and Complex Wavelet Transform. But they are more expansive and this can be compromised by Double Density Wavelet Transform. Image fusion can be performed using three levels namely Pixel, feature and decision level. This paper evaluates the performance of feature level fusion of multi focused images using Discrete, Stationary and Dual Tree Complex wavelet transform in terms of various performance measures.

Matched DDWT ROI Compression Engine for the Imaging Particle Detector

A new multirate DDWT (Double Density Wavelet Transform) matched filter ROI (region of interest) processor for the ICARUS (Imaging Cosmic And Rare Underground Signals) particle detector [Amerio(2004)] has been designed. The ROI extraction engine is based on matched and unmatched Wiener filtering using coupled DWT and DDWT processing. High through-put image-like ICARUS detector data (160MBps data rate per crate) is able to be compressed 600 times using multi-stage compression with ROI extraction. The 32-channel (160MBps data rate) processor has been fully implemented in a low cost FPGA device thanks to Fast Integer Arithmetic Wavelet Transform (FIAWT) [Pólchlopek(2006)] algorithm implementation.

A New Denoising System for SONAR Images

The SONAR images are perturbed by speckle noise. The use of speckle reduction filters is necessary to optimize the image exploitation procedures. This paper presents a new denoising method in the wavelet domain, which tends to reduce the speckle, preserving the structural features and textural information of the scene. Shift-invariance associated with good directional selectivity is important for the use of a wavelet transform (WT) in many fields of image processing. Generally, complex wavelet transforms, for example, the Double Tree Complex Wavelet Transform (DT-CWT) have these useful properties. In this paper, we propose the use of the DT-CWT in association with Maximum A Posteriori (MAP) filters. Such systems carry out different quality denoising in regions with different homogeneity degree. We propose a solution for the reduction of this unwanted effect based on diversity enhancement. The corresponding denoising algorithm is simple and fast. Some simulation results prove the performance obtained.

Iris recognition using multi-resolution transforms

Iris recognition is one of the most reliable personal identification methods and it plays a very important role in forensic and civilian applications. In this paper, an efficient multi-resolution based iris recognition system with high performance and less complexity is described. The system includes pre processing iris images, determining the pupil centre, converting the iris boundary to the stretched polar coordinate system, extracting the iris code based on texture analysis using multi-resolution transforms, such as Discrete Wavelet Transform (DWT), Double Density Wavelet Transform (DDWT) and Dual Tree Complex Wavelet Transform (DTCWT). A fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences in iris code is used for classification.

Undecimated double density wavelet transform based speckle reduction in SAR images

This paper describes an efficient and adaptive method of threshold estimation for removing Speckle noise from Synthetic Aperture Radar (SAR) images, based on Undecimated Double Density Wavelet Transform (UDDWT). Here the performance of image denoising algorithm is well improved by fixing different optimum threshold values for each wavelet coefficient. The choice of the estimation of the threshold value is carried out by analyzing the statistical parameters of the wavelet subband coefficients like Arithmetic Mean, Geometric Mean and Standard Deviation. Here the image is first decomposed into many subbands using UDDWT. Then based upon the statistical parameters of the wavelet coefficients of subbands, threshold values are found out for each wavelet coefficients. This threshold value is used in Soft Thresholding Technique to remove the noisy wavelet coefficients. Then the inverse transform is applied to get the denoised image. Evaluation parameters like peak signal to noise ratio, standard deviation to mean ratio and Edge Preservation Factor have been used for evaluating the performance of the proposed technique quantitatively. Experimental results on several benchmark images by using the proposed method show that, the proposed method yields significantly superior image quality. Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm.

IMAGE DE-NOISING USING DOUBLE DENSITY WAVELET TRANSFORM BASED ADAPTIVE THRESHOLDING TECHNIQUE

This paper describes an efficient and adaptive method of threshold estimation for removing impulse noise from images, based on Double Density Wavelet Transform (DDWT). The performance of image de-noising algorithms using wavelet transforms can be improved significantly by fixing an optimum threshold value, based on the analysis of the statistical parameters of subband coefficients. In this proposed method, the choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet subband coefficients like standard deviation, arithmetic mean and geometrical mean. Here the noisy image is first decomposed into many levels to obtain different frequency bands using DDWT. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum threshold value by the proposed method. Experimental results on several test images by using the proposed method show that, the proposed method yields significantly superior image quality and better Peak Signal-to-Noise Ratio (PSNR). Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm.

ArH + near 5 μm with high resolution double modulation FTS

High resolution double modulation Fourier Transform Spectroscopy (FTS) is demonstrated for the first time. It is applied to velocity modulation experiments. Lines emitted from ions are successfully selected. The Δ v=1 sequence of the ArH + ions is recorded up to the 7–6 vibrational transition. Preliminary measurements of the Doppler-shifts show no vibrational effects from the Δ v=1 sequence vibrational bands and most likely no vibrational state-to-state dependence of the ArH + ion mobility.

Analysis of Wind Field Considering the Effects of Stratified Atmosphere in Mountainous Area

A new analytical method of wind field considering stratified atmosphere is presented. The analytical solutions of wind u, v, and w were obtained by using the perturbation method and double Fourier Transform for the basic equations. The solutions can be applied for arbitrary topography of basins in meso-βA scale. The results of analysis show following characteristics. 1) Ascending wind is suppressed by stratification effects of atmosphere comparing with potential flow. 2) In the case of the range of mountain, it is shown that blocking appears in the case of less than the critical internal Froude number. 3) In the case of internal Froude number sufficiently larger than the critical number, the solution approarchs asymptotically the one for potential flow.

The nature of the second dimension in 2D NMR

AbstractA two‐dimensional (2D) NMR spectrum is produced from the double Fourier Transform (FT) of two time domains, t1 and t2. One of these domains, t2, is identical to the acquisition time used to collect a Free Induction Decay (FID) in one‐dimensional (1D) NMR spectroscopy. After performing the FTs of a series of these FIDs, an array of resonance lines is obtained whose amplitudes and phases are a function of t1. The t1 domain is then composed of another series of “FIDs” that are related to those collected during t2. In this article, it is shown that in a heteronuclear‐J, δ gated‐decoupling experiment, the “FIDs” in the t1 domain produce, after the FT, proton‐coupled spectra whose sum is identical to that obtained from a 1D carbon FID collected with no proton decoupling.