Discrete Wavelet Domain Research Articles

Color Channel Normalization and Fuzzy Filter based Image Fusion in Discrete Wavelet Domain

In Multi-focus image fusion has appeared as a significant subject in computer perspective and picture handling neighborhood because the visual contacts have a decreasing target range. Multi-focus picture fusion method have been commonly utilised in machine vision, targeting, object acceptance, medical imaging and military affairs. In nearly all past image fusion study, the origin images are thought as completely aligned. But still it has some issues which are neglected for image fusion. This paper proposes two level Discrete Wavelet Transformation (DWT). The proposed methodology has indicated really significant improvements around available ones.

Telltale Watermarks for Counting JPEG Compressions

Telltale Watermarks for Counting JPEG Compressions

Wavelet Transform - A New Tool for Analysis of Harmonics in Power Systems

This paper presents a review of application of discrete wavelet transform in power system transient analyses. Based on the discrete time domain approximation, the system components such as resistor and inductor are modeled respectively in discrete wavelet domain for the purpose of transient and steady state analyses. Numerical results for transient inductor model can be implemented by any kind of power system including normal and emergency operating modes.

English

English

Face Recognition Algorithm Using Two Dimensional Locality Preserving Projection in Discrete Wavelet Domain

This paper proposes a new method for face recognition using Two Dimensional Locality Preserving Projection (2DLPP) in Discrete Wavelet Domain (DWT). Firstly, the dimensionality of the original face image is reduced by using the discrete wavelets transform and low frequency channel (LL) is selected to be as the features of face image. And then 2DLPP is applied to decrease the dimension of LL. Finally, the nearest neighbour classifier is used for face classification. The experiment results on ORL face database demonstrate the effectiveness of the proposed method.

Multiscale Hybrid Nonlocal Means Filtering Using Modified Similarity Measure

A new multiscale implementation of nonlocal means filtering (MHNLM) for image denoising is proposed. The proposed algorithm also introduces a modification of the similarity measure for patch comparison. Assuming the patch as an oriented surface, the notion of a normal vectors patch is introduced. The inner product of these normal vectors patches is defined and then used in the weighted Euclidean distance of intensity patches as the weight factor. The algorithm involves two steps: the first step is a multiscale implementation of an accelerated nonlocal means filtering in the discrete stationary wavelet domain to obtain a refined version of the noisy patches for later comparison. The next step is to apply the proposed modification of standard nonlocal means filtering to the noisy image using the reference patches obtained in the first step. These refined patches contain less noise, and consequently the computation of normal vectors and partial derivatives is more precise. Experimental results show equivalent or better performance of the proposed algorithm compared to various state-of-the-art algorithms.

Hyper-spectral Remote Sensing Image Compression Based on Nonnegative Tensor Factorizations in Discrete Wavelet Domain

该文提出一种基于非负张量分解的高光谱图像压缩算法。首先将高光谱图像的每个谱段进行2维离散5/3小波变换，消除高光谱图像的空间冗余。然后将所有谱段的每级小波变换的4个小波子带看作为4个张量。对每个小波子带张量采用改进HALS(Hierarchical Alternating Least Squares)算法进行非负分解，来消除光谱冗余和空间残余冗余，同时保护了光谱信息。最后，将分解的因子矩阵进行熵编码。实验结果表明，该文提出的压缩算法具有良好压缩性能，在压缩比32:1~4:1范围内，平均信噪比高于40 dB，与传统高光谱图像压缩算法比较，平均峰值信噪比提高了1.499 dB。有效地提高了高光谱图像压缩算法的压缩性能和保护了光谱信息。

Unsupervised SAR Image Segmentation Based on a Hierarchical TMF Model in the Discrete Wavelet Domain for Sea Area Detection

Unsupervised synthetic aperture radar (SAR) image segmentation is a fundamental preliminary processing step required for sea area detection in military applications. The purpose of this step is to classify large image areas into different segments to assist with identification of the sea area and the ship target within the image. The recently proposed triplet Markov field (TMF) model has been successfully used for segmentation of nonstationary SAR images. This letter presents a hierarchical TMF model in the discrete wavelet domain of unsupervised SAR image segmentation for sea area detection, which we have named the wavelet hierarchical TMF (WHTMF) model. The WHTMF model can precisely capture the global and local image characteristics in the two-pass computation of posterior distribution. The multiscale likelihood and the multiscale energy function are constructed to capture the intrascale and intrascale dependencies in a random field (X,U). To model the SAR data related to radar backscattering sources, the Gaussian distribution is utilized. The effectiveness of the proposed model for SAR image segmentation is evaluated using synthesized and real SAR data.

Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization

Reducing scanning time is significantly important for MRI. Compressed sensing has shown promising results by undersampling the k-space data to speed up imaging. Sparsity of an image plays an important role in compressed sensing MRI to reduce the image artifacts. Recently, the method of patch-based directional wavelets (PBDW) which trains geometric directions from undersampled data has been proposed. It has better performance in preserving image edges than conventional sparsifying transforms. However, obvious artifacts are presented in the smooth region when the data are highly undersampled. In addition, the original PBDW-based method does not hold obvious improvement for radial and fully 2D random sampling patterns. In this paper, the PBDW-based MRI reconstruction is improved from two aspects: 1) An efficient non-convex minimization algorithm is modified to enhance image quality; 2) PBDW are extended into shift-invariant discrete wavelet domain to enhance the ability of transform on sparsifying piecewise smooth image features. Numerical simulation results on vivo magnetic resonance images demonstrate that the proposed method outperforms the original PBDW in terms of removing artifacts and preserving edges.

A NOVEL APPROACH IN IMAGE WATERMARKING WITH DISCRETE WAVELET TRANSFORM

In this paper we present a new watermarking scheme for still image data. Most of the recent work in watermarking can be grouped into two categories: spatial domain methods and frequency domain methods. We introduce a novel approach of watermarking which involves embedding the watermark in the discrete wavelet domain. We make use of a multi resolution data fusion approach in which the image and watermark are both transformed into the discrete wavelet domain. The resulting image pyramids are then fused according to a series of combination. After watermark insertion, inverse DWT is applied to the sub-bands with modified coefficients to obtain the watermarked image. For watermark extraction, a threshold-based decoder is designed. Embedding and extraction process are characterized with parameters and genetic algorithm is used for parameter optimization. Optimization is to maximize the values of peak signal-to-noise ratio of the watermarked image and normalized cross correlation of the extracted watermark. The performance of the proposed scheme is compared with the existing schemes and significant improvement is observed.

Integrated Normalized Content System for Efficient Watermarking

In contemporary years, different watermarking schemes are proposed for image authentication and copyright protection. In images, any tiny change to the content is not acceptable; the embedding distortion has to be compensated for perfectly. In this paper a powerful Image watermarking method using DWT based on integrated normalized content based chaotic (DWTINCC) system is proposed, to obtain better digital content authentication, originality, higher embedding capacity, quality of the information and better robustness. The objective of the proposed integrated scheme is to examine how content image normalization and discrete wavelet domain based chaotic system procedure are used to enhance robustness of a digital image watermark especially for statistical attacks. The proposed integrated scheme consists of four stages. In the stage two image normalization is performed, to achieve the scaling and rotational invariance. In stage two DWT is applied on the normalized image. In stage three a block based content authentication scheme is adopted on the normalized image of stage one for image authentication and tamper localization. In stage four chaotic models is adopted on the stage three to achieve excellent robustness against any statistical attacks and to increase the security of the digital watermarking system. The experimental result shows that the proposed DWT-INCC method is more robust and effective even in the presence of various attacks when compared with many existing methods.

DWT 영역에서의 주파수 정보를 활용한 가변 윈도우 기반의 스테레오 정합 알고리즘

In this paper we propose a new stereo matching algorithm which is suitable for application to obtain depth information with high-speed in stereoscopic camera environment. For satisfying these condition we propose a new adaptive stereo matching technique using frequency information in discrete wavelet (DWT) domain and variable matching window. The size of the matching window is selected by analysis of the local property of the image in spatial domain and the feature and scaling factor of the matching window is selected by the frequency property in the frequency domain. For using frequency information we use local DWT and global DWT. We identified that the proposed technique has better peak noise to signal ratio (PSNR) than the fixed matching techniques with similar complexity.

Image Super Resolution Enhancement Based on Interpolation of Discrete and Stationary Wavelet Domain

In this paper, we propose an image super resolution enhancement technique based on interpolation of the high frequency sub band images obtained by discrete wavelet transform (DWT) using different types of wavelets such as Daubechies 1. Daubechies 2 .., Daubechies 9 haar, and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). We compare the results of different types of wavelets. DWT is applied in order to decompose an input image into different sub bands. Then the high frequency sub bands as well as the input image are interpolated. The estimated high frequency sub bands are being modified by using high frequency sub bands obtained through SWT then all these sub bands are combined to generate a new super-resolved image by using inverse DWT.

A Novel Discrete Wavelet Domain Error-Based Image Quality Metric with Enhanced Perceptual Performance

I n this paper, we present an error-based method to compute image quality scores in the discrete wavelet domain using the Haar wavelet. The proposed method does not extract any human visual system parameters, which noticeably reduces its complexity, and relies on computation of the errors (differences) at an appropriate level of wavelet decomposition. We also propose a formula to automatically calculate the required level of wavelet decomposition at a desired viewing distance. To consider the effect of very fine image details in quality assessment, the proposed method defines a multi-level edge map for each image which comprises only the most informative image subbands. We present the mathematical complexity analysis of our method and compare it to the conventional PSNR. We show that when the defined edge map is not taken into account we can calculate the quality score with lower complexity than with the PSNR, and with a prediction accuracy close to that of SSIM. We tested the performance of our method on two different databases: an image database and a video database.

A New Approach in Digital Image Watermarking Using Fractal Model in DWT Domain

The recent progress in the digital multimedia technologies has offered many facilities in the transmission, reproduction and manipulation of data. However, this advance has also brought the problem such as copyright protection for content providers. Digital watermarking is one of the proposed solutions for copyright protection of multimedia. This paper proposes a blind watermarking algorithm based on fractal model in discrete wavelet domain for copyright protection. The idea of the presented scheme is to hide a binary image as a watermark with fractal parameters in wavelet domain of host image. Fractal compression technique is used to encode a gray image and fractal codes are embedded into the wavelet coefficients of the gray image according to well-connected watermark algorithm. The experimental results show that the algorithm is robust against geometric and non geometric attacks.

A novel discrete wavelet transform framework for full reference image quality assessment

In this paper, we present a general framework for computing full reference image quality scores in the discrete wavelet domain using the Haar wavelet. In our framework, quality metrics are categorized as either map-based, which generate a quality (distortion) map to be pooled for the final score, e.g., structural similarity (SSIM), or nonmap-based, which only give a final score, e.g., Peak signal-to-noise ratio (PSNR). For map-based metrics, the proposed framework defines a contrast map in the wavelet domain for pooling the quality maps. We also derive a formula to enable the framework to automatically calculate the appropriate level of wavelet decomposition for error-based metrics at a desired viewing distance. To consider the effect of very fine image details in quality assessment, the proposed method defines a multi-level edge map for each image, which comprises only the most informative image subbands. To clarify the application of the framework in computing quality scores, we give some examples to show how the framework can be applied to improve well-known metrics such as SSIM, visual information fidelity (VIF), PSNR, and absolute difference. The proposed framework presents an excellent tradeoff between accuracy and complexity. We compare the complexity of various algorithms obtained by the framework to the IPP-based H.264 baseline profile encoding using C/C++ implementations. For example, by using the framework, we can compute the VIF at about 5% of the complexity of its original version, but with higher accuracy.

Data Hiding in Images in Discrete Wavelet Domain Using PMM

Over last two decades, due to hostilities of environment over the internet the concerns about confidentiality of information have increased at phenomenal rate. Therefore to safeguard the information from attacks, number of data/information hiding methods have evolved mostly in spatial and transformation domain.In spatial domain data hiding techniques,the information is embedded directly on the image plane itself. In transform domain data hiding techniques the image is first changed from spatial domain to some other domain and then the secret information is embedded so that the secret information remains more secure from any attack. Information hiding algorithms in time domain or spatial domain have high capacity and relatively lower robustness. In contrast, the algorithms in transform domain, such as DCT, DWT have certain robustness against some multimedia processing.In this work the authors propose a novel steganographic method for hiding information in the transform domain of the gray scale image.The proposed approach works by converting the gray level image in transform domain using discrete integer wavelet technique through lifting scheme.This approach performs a 2-D lifting wavelet decomposition through Haar lifted wavelet of the cover image and computes the approximation coefficients matrix CA and detail coefficients matrices CH, CV, and CD.Next step is to apply the PMM technique in those coefficients to form the stego image. The aim of this paper is to propose a high-capacity image steganography technique that uses pixel mapping method in integer wavelet domain with acceptable levels of imperceptibility and distortion in the cover image and high level of overall security. This solution is independent of the nature of the data to be hidden and produces a stego image with minimum degradation. Keywords—Cover Image, Pixel Mapping Method (PMM), Stego Image,Integer Wavelet Tranform.

Segmentation of typhoon cloud image by combining a discrete stationary wavelet transform with a continuous wavelet transform

An algorithm is proposed that employs a multi-threshold technique to segment a typhoon cloud image. For both reducing the noise and enhancing the detail in the typhoon cloud image, the noise is reduced by a Wiener filter and the detail is enhanced by a nonlinear gain operator in the discrete stationary wavelet domain in the proposed algorithm. Then, the histogram equalization technique is used to enhance the global contrast of the processed image. In order to reduce the false peaks of the histogram of the denoised and enhanced typhoon cloud image (DETCI), a Bezier curve is used to smooth the histogram. An optimal segmentation threshold is then obtained from the multiple thresholds obtained from the Bezier histogram. The optimal threshold is used to segment the DETCI. The region of the maximum area in the segmented DETCI is selected as a region of interest (ROI). Thus other objects of small cloud masses are removed by the above method. We replace the area of the ROI with the corresponding area of the original typhoon cloud image to obtain the segmented ROI (SROI). Again the Bezier histogram is used to smooth the false peaks in the histogram of the SROI. In order to detect accurately the peaks and valleys in the curve of the Bezier histogram, a continuous wavelet transform is used to determine the location of peaks and valleys. After the wavelet transform, multi-segmented images at different scales are obtained. A criterion is employed to select an optimal segmentation scale. Finally, the whole typhoon cloud series is segmented accurately by the proposed method. Experimental results show that the proposed algorithm can efficiently segment the typhoon cloud series from a typhoon cloud image, and is better than the Olivo and HQ methods for analysing the structure of the typhoon wind field.

Robust background subtraction in traffic video sequence

For intelligent transportation surveillance, a novel background model based on Marr wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.

A New Approach to Nonsinusoidal Steady‐State Power System Analysis

A new analysis method using wavelet domain for steady‐state operating condition of power system is developed and introduced. Based on wavelet‐Galerkin theory, the system components such as resistor, inductor, capacitor, transmission lines, and switching devices are modeled in discrete wavelet domain for the purpose of steady‐state analysis. To solve system equations, they are transferred to wavelet domain by forming algebraic matrix‐vector relations using the wavelet transform coefficients and the equivalent circuit is thus built for system simulation. After describing the new algorithm, two‐case studies are presented and compared with the simulations in the time domain to verify the accuracy and computational performance.