Wavelet Coefficients Of Image Research Articles

An Efficient Algorithm for Multimodal Medical Image Fusion based On Feature Selection and PCA Using DTCWT

Background: In the two past decades, medical image fusion has become an essential part of modern medicine due to the availability of numerous imaging modalities (MRI, CT, SPECT. etc). This paper presents a new medical image fusion algorithm based on DTCWT and uses different fusion rules in order to obtain a new image which contains more information than any of the input images. Methods: In order to improve the visual quality of the fused image, we propose a new image fusion algorithm based on Dual Tree Complex Wavelet Transform (DTCWT). Using different fusion rules in a single algorithm leads to a perfect reconstruction of the output (fused image).This combination will create a new method which exploits the advantages of each method separately. DTCWT present good directionality since it considers the edge information in six directions and provides approximate shift invariant. The goal of Principal Component Analysis (PCA) is to extract the most significant features (wavelet coefficients in our case) in order to improve the spatial resolution. The proposed algorithm fuses the detailed wavelet coefficients of input images using features selection rule. Results: We have conducted several experiments over different sets of multimodal medical images such as CT/MRI, MRA/T1-MRI; however, only results of two sets have been presented (due to pages-limit). The proposed fusion algorithm is compared to recent fusion methods presented in the literature (eight methods) in terms of visual quality and quantitatively using well known fusion performance metrics (five metrics). Results showed that the proposed algorithm outperforms the existing ones in terms of visual and quantitative evaluations. Conclusion: This paper focuses on image fusion of medical images obtained from different modalities. We have proposed a novel algorithm based on DTCWT in order to merge multimodal medical images. Experiments have been performed over two different sets of multimodal medical images. The results show that the proposed method significantly outperforms other techniques reported in the literature.

LWT–DSR based new robust framework for watermark extraction under intentional attack conditions

This paper proposes a new watermarking approach using dynamic stochastic resonance (DSR) based tuning operation to extract the watermark logo from the watermarked image that has undergone different intentional and signal processing attacks. This method is intended to provide remedies from the shortcomings of the technique proposed by Lin et al. (2008), and invalidates the effect of intentional attacks recently designed by Meerwald et al. (2009). The algorithm incorporates three level image decomposition using lifting wavelet transform (LWT) and low-pass subband is utilized for data hiding purpose. Watermark bits are embedded into the blocks of non-overlapped wavelet coefficients of the cover image by quantizing the two maximum coefficients of the corresponding block. In watermark extraction process, the DSR is applied by performing the tuning operation on coefficient blocks of attacked watermarked image. It is a parameter dependent approach that enhances the performance of watermark extraction, where the parameters of DSR inherently depend on the image properties such as standard deviation or variance. As far as security is concerned, the randomization of wavelet coefficients, blocks, and watermark bits helps the framework to be more secure. The proposed technique is also examined against multiple watermarking attack and successfully proves its authenticity and ownership. Comparison of the proposed technique with recent techniques shows remarkable improvement in terms of robustness and security against various intentional, signal processing, and geometrical attacks.

A hybrid watermarking scheme with CS theory for security of multimedia data

A hybrid watermarking scheme for multimedia data such as digital image, digital video is proposed in this paper. The scheme utilizes various image processing transforms and Compressive Sensing (CS) to achieved fragility and security for multimedia data. The compressive sensing is applied on the singular value of wavelet coefficients of watermark image to get the CS measurements. These CS measurements are embedded with embedding factor into the hybrid coefficients (high frequency curvelet coefficients of DT coefficients) of the host medium. In the proposed scheme, host medium may be digital image or video. The generated watermarked data have different security layers such as four levels from CS such as knowledge of transform basis matrix, measurement matrix, orthogonal matrices, information of wavelet basis matrix and one level from watermarking such as embedding factor. The scheme offers fragility and security to multimedia data against various standard watermarking attacks such as signal processing attacks, geometric attacks, and compression attacks. This scheme can be provided high payload capacity and used for host multimedia authentication. A comparison of proposed scheme with existed schemes shows that the proposed scheme performed faster and better than existed schemes available in the literature.

EMD-based steganography techniques for JPEG2000 encoded images

In JPEG2000 standard, the number of bit planes of wavelet coefficients to be used in encoding is dependent on the compression ratio as well as subbands. These significant wavelet bit planes can be utilized to embed bits of secret data as they are retained in the final bit stream after Tier-2 encoding. In proposed techniques, the above mentioned concept have been utilized to embed secret data bits in lowest significant bit planes of the quantized wavelet coefficients of a cover image. In first technique, secret data is converted into a series of symbols using multiple bases notational system. These bases are selected by using the degree of local variation of coefficient values of the cover image so that coefficient of a complex region can potentially carry more secret data bits as compared to coefficients of smooth region. Symbols of secret data are embedded into bit planes of significant quantized wavelet coefficients by using EMD approaches. In second technique, the secret data bits are embedded into significant quantized wavelet coefficients by using modified EMD. Experimental results show that these proposed techniques provide large embedding capacity and better visual quality stego images than existing steganography techniques applicable to JPEG2000 compressed images. It has also been shown that modified EMD-based technique is better than EMD with multiple bases notational system (MBNS)-based technique.

ROBUST WATERMARKING TECHNIQUE USING DIFFERENT WAVELET DECOMPOSITION LEVELS FOR SIGNATURE IMAGE PROTECTION

This paper proposed a non-blind watermarking technique based on different wavelet decomposition levels for biometric image protection. In this technique, a biometric image is used as a watermark instead of a standard image, logo or random noise pattern type watermark. For watermark embedding, the original host image and the watermark biometric image are transformed into various levels of wavelet coefficients. The watermark biometric image is embedded into the host image by modifying the values of the wavelet coefficients of the host image using the values of wavelet coefficients of the watermark biometric image. Experimental results demonstrated that the proposed technique was able to withstand various watermarking attacks. The novelty of the proposed technique is that it is used to transform coefficients of the watermark biometric image instead of the Pseudo Noise sequences or any other feature extraction technique.

Comparative study of statistical modeling of wavelet coefficients for SAR image despeckling

The work is concentrated to get good despeckled performance under non-homomorphic framework by involving simple distributions to obtain closed form Maximum-a-Posteriori probability (MAP) solutions. To estimate noise-free wavelet coefficient, distributions with lesser number of parameters allied Laplacian/Gaussian probability density function (pdf) for signal reflectivity and Rayleigh/Gaussian pdf for noisy signal are employed. Thus, the four despeckling methods are formed, namely LRMAP, GRMAP, LGMAP and GGMAP to despeckle SAR images and the effectiveness of different distributions is studied. The despeckling method is made adaptive by estimating the local variance of high frequency image using wavelet sub-band coefficient statistics. Also, the parameter space used in the proposed methods does not involve initialization, iterative search and convergence problems. The performances of the proposed methods are evaluated in terms of Equivalent Number of Looks (ENL), Peak Signal to Noise Ratio (PSNR), Edge Preservation ([Formula: see text] and Mean Structural Similarity Index Measure (MSSIM). Experimental results show that LRMAP yields good results over all methods and GGMAP does not perform good for all images in terms of all quality metrics. Also, the proposed methods yield good quality metrics in less computing time as compared with the method available in the literature.

Model-Based Nonuniform Compressive Sampling and Recovery of Natural Images Utilizing a Wavelet-Domain Universal Hidden Markov Model

In this paper, a novel model-based compressive sampling (CS) technique for natural images is proposed. Our algorithm integrates a universal hidden Markov tree (uHMT) model, which captures the relation among the sparse wavelet coefficients of images, into both sampling and recovery steps of CS. At the sampling step, we employ the uHMT model to devise a nonuniformly sparse measurement matrix $\boldsymbol{\Phi }_{\text{uHMT}}$ . In contrast to the conventional CS sampling matrices, such as dense Gaussian, Bernoulli or uniformly sparse matrices that are oblivious to the signal model and the correlation among the signal coefficients, the proposed $\boldsymbol{\Phi }_{\text{uHMT}}$ is designed based on the signal model and samples the coarser wavelet coefficients with higher probabilities and more sparse wavelet coefficients with lower probabilities. At the recovery step, we integrate the uHMT model into two state-of-the-art Bayesian CS recovery schemes. Our simulation results confirm the superiority of our proposed HMT model-based nonuniform compressive sampling and recovery , referred to as uHMT-NCS , over other model-based CS techniques that solely consider the signal model at the recovery step. This paper is distinguished from other model-based CS schemes in that we take a novel approach to simultaneously integrating the signal model into both CS sampling and recovery steps. We show that such integration greatly increases the performance of the CS recovery, which is equivalent to reducing the required number of samples for a given reconstruction quality.

Comparision of Blur Detection and Segmentation Techniques

Objectives: Increasing the quality of the captured image by using different blur detection techniques and making the picture into pixels to redefine the image. Distortion identification based image integrity and verity evaluation which organizes natural scene data of image wavelet co-efficient. Methods/Statistical Analysis: To improve image quality, the different blur detection techniques used in this paper are namely blind image de-convolution, two-stage image segmentation, edge sharpness analysis, non-reference NR block, no directional high frequency. According to these techniques and their procedures, it is estimated that blind image de-convolution is best because it reduces the need for future engineering and identifies the blur type for the mixed input of image for various parameters. Findings: Images are taken around many parts and are used to store and show the information which is precise useful. But many periods the quality of the pictures that are captured is not well-intentioned. The blur detection is initiate helpful in the real life applications and are established in the areas of image segmentation, image restoration. The growth of the blur detection practices have improved the various systems to remove the blur or un-focused part from the image which is owed to imperfection of the camera or due to the de-focus of the gesture of the portion, extreme strength of light. This paper suggests the sharpness, quality image that are in out-of-focus areas. Here this paper proposes the blind image de-convolution method is finest to detect the blur image in the numerous aspects of the sections and the parameters. The outcomes of the segmentation and blur detection practices are compared based on the computational time, cost and the advantages and disadvantages that are projected in the practices. The blur image detection procedures used in this paper are Blind image de-convolution, Two stage image segmentation method,, Non-reference (NR) block, Low directional high frequency energy (for motion blur), edge sharpness analysis. Application/Improvements: Blur detection and segmentation techniques is used to eliminate blur from image source and take out the just right quality of the image using techniques that is proposed in this paper. The comparison made in this paper shows that blur detection techniques which has low computational time and Root Mean Square Error that is frequently used to calculate the differences between pixel value of the image.

Adaptive Spatial Steganography Based on the Correlation of Wavelet Coefficients for Digital Images in Spatial Domain

How to define the pixel’s embedding distortion is a key problem of image adaptive steganography. This paper proposes an adaptive spatial steganography in spatial domain based on the correlation of wavelet coefficients for digital images according to the principle of minimizing a suitable defined distortion. Firstly, one-dimensional high-pass and low-pass filters are used to construct directional filters. Secondly, do direction filtering to images through the direction of horizontal, vertical and diagonal, and design the distortion cost function according to the correlation of wavelet coefficients. Finally, the secret message is embedded by STC (Syndrome Trellis Code) according to the pixel’s embedding distortion. The experiment results illustrate that the proposed method not only make the embedding region focus on the texture regions whose content is complex, but also can resist the common steganalysis detection effectively.

A group matching pursuit for image reconstruction

Matching Pursuit (MP) is a fast and effective sparse representation algorithm, so it and its improved algorithms are used to solve the problem of Compressive Sensing (CS) reconstruction. MP finds the support of the unknown signal sequentially based on the correlation values between the basis vectors and the measurement vector. As the sampling rate decreases, the signal could not be reconstructed successfully. The nature image wavelet coefficients always remain a residual dependency structure which we can use to improve the CS reconstruction, such as an aggregation of neighborhood and the significant coefficients appear at the locations of the image edges. Make full use of the priors are mentioned above, we propose a Group Matching Pursuit (GMP) algorithm base on the edge. In GMP, with the neighborhood structure employed as a spatial constraint, the coefficients are organized as groups to restrain each other. Then the extracted image edge is used as the prior information to improve the reconstruction quality. Finally, we propose a Bayesian Group Matching Pursuit (BGMP) algorithm. In BGMP the group coefficients are modeled by a multivariate Gaussian distribution, and solved by a maximum a posteriori probability (MAP) estimate. Experiments have shown that, the methods based on GMP have a better reconstruction in solving the reconstruction problem of CS.

Automatic no-reference image quality assessment.

No-reference image quality assessment aims to predict the visual quality of distorted images without examining the original image as a reference. Most no-reference image quality metrics which have been already proposed are designed for one or a set of predefined specific distortion types and are unlikely to generalize for evaluating images degraded with other types of distortion. There is a strong need of no-reference image quality assessment methods which are applicable to various distortions. In this paper, the authors proposed a no-reference image quality assessment method based on a natural image statistic model in the wavelet transform domain. A generalized Gaussian density model is employed to summarize the marginal distribution of wavelet coefficients of the test images, so that correlative parameters are needed for the evaluation of image quality. The proposed algorithm is tested on three large-scale benchmark databases. Experimental results demonstrate that the proposed algorithm is easy to implement and computational efficient. Furthermore, our method can be applied to many well-known types of image distortions, and achieves a good quality of prediction performance.

Reduced-reference image quality assessment using moment method

ABSTRACTReduced-reference image quality assessment (RR IQA) aims to evaluate the perceptual quality of a distorted image through partial information of the corresponding reference image. In this paper, a novel RR IQA metric is proposed by using the moment method. We claim that the first and second moments of wavelet coefficients of natural images can have approximate and regular change that are disturbed by different types of distortions, and that this disturbance can be relevant to human perceptions of quality. We measure the difference of these statistical parameters between reference and distorted image to predict the visual quality degradation. The introduced IQA metric is suitable for implementation and has relatively low computational complexity. The experimental results on Laboratory for Image and Video Engineering (LIVE) and Tampere Image Database (TID) image databases indicate that the proposed metric has a good predictive performance.

Image interpolation for high-resolution display based on the complex dual-tree wavelet transform and hidden Markov model

This paper presents a new method for the interpolation of a full high-definition (HD) image based on the dual-tree complex wavelet transform (DT-CWT) and hidden markov model (HMM). In the proposed method, the DT-CWT is used to decompose the low-resolution image into different subbands. In wavelet domain interpolation, given image is assumed as the low frequency LL subband of the wavelet coefficients of a high-resolution image. The proposed method estimates the higher band coefficients by learning the correlation between the coefficients across the scale. In this paper, the relationship between the wavelet coefficients across the scale is described by HMM, and each wavelet coefficient is modeled by a Gaussian mixture having multiple means and variances. Experimental results show that the proposed algorithm yields images that are sharper compared to several other methods that we have considered in this paper.

The Application of Wavelet-Domain Hidden Markov Tree Model in Diabetic Retinal Image Denoising.

The wavelet-domain Hidden Markov Tree Model can properly describe the dependence and correlation of fundus angiographic images’ wavelet coefficients among scales. Based on the construction of the fundus angiographic images Hidden Markov Tree Models and Gaussian Mixture Models, this paper applied expectation-maximum algorithm to estimate the wavelet coefficients of original fundus angiographic images and the Bayesian estimation to achieve the goal of fundus angiographic images denoising. As is shown in the experimental result, compared with the other algorithms as mean filter and median filter, this method effectively improved the peak signal to noise ratio of fundus angiographic images after denoising and preserved the details of vascular edge in fundus angiographic images.

Non Oblivious Watermarking Technique for JPEG2000 Compressed Images Using Arnold Scrambling of Unequal Size Watermark Blocks

In this paper, a watermarking technique for JPEG 2000 compressed image is proposed. Scrambling of secret message is performed block-wise using Arnold Transform. Secret message is divided into non-overlapping blocks of unequal size and then Arnold transform is applied on each block and secret key is generated based on the periodicity of each block. Scrambled secret message is embedded into qualified significant wavelet coefficients of a cover image. After embedding the secret message into wavelet coefficients, the remaining processes of JPEG 2000 standard are executed to compress the watermarked image at different compression rates. Scaling Factor ( SF ) is used to embed watermark into wavelet coefficients and the value of SF is stored into COM box of the code stream of JPEG 2000 compressed image and this SF value and secret key are used to extract the embedded watermark on the receiver side. The performance of the proposed technique is robust to a variety of attacks like image cropping, salt and pepper noise, and rotation. Proposed technique is compared with the existing watermarking techniques for JPEG 2000 compressed images to show its effectiveness.

Dither modulation of significant amplitude difference for wavelet based robust watermarking

This paper proposed a wavelet quantization based method for robust watermarking with resistance to incidental distortions. For transform domain based watermarking algorithms, blindly localizing adequate significant coefficients is one critical issue to guarantee the robustness while preserving good fidelity. In the proposed method, low frequency wavelet coefficients of the host image are randomly permutated into sub-groups according to a watermarking secret key. Embedding modifications are then distributed to important coefficients which preserve large perceptual capacity by quantizing the significant amplitude difference (SAD). Meanwhile, dither modulation strategy is employed to control the quantization artifacts and increase the robustness. In such a framework, the blind watermark extraction can be straightforwardly achieved with the watermarking secret keys which only shared by the embedder and extractor for advanced security. Numerous comparison experiments are conducted to evaluate the watermarking performance. Experimental results demonstrate the superiority of our scheme on robustness against content-preserving operations and incidental distortions such as JPEG compression, Gaussian noise.

Sparse Watermarking Technique for Improving Security of Biometric System

Abstract Any biometric system is vulnerable against various attacks. One of the attacks is on the communication channel between the system database and matcher module of the biometric system. Therefore, this paper proposed a sparse watermarking technique based on compressive sensing (CS) theory framework for security against this kind of attack. In this proposed sparse watermarking technique, fingerprint image is taken as watermark information. This fingerprint image is converted into sparse measurements using compressive sensing (CS) theory and embeds these sparse measurements into wavelet coefficients of standard image to generate a watermarked image. The structural similarity measure index (SSIM) quality measure is used for authentication between original watermark fingerprint image and reconstructed watermark fingerprint image. The experimental results show that proposed sparse watermarking technique does not affect verification and authentication performance of a biometric system. The proposed sparse watermarking technique has a good performance compared to the existing watermarking technique in the literature.

Denoising Ultrasound Medical Images with Selective Fusion in Wavelet Domain

Abstract Ultrasound medical imaging is undoubtedly an incontestable tool which provides the view of the internal organs of a body. The no ionizing radiation exposure property of the ultrasound medical images made it more fitting for fetus imaging. The only major snag ultrasound imaging encompasses is speckle noise that results from constructive and destructive interference thereby degrading the quality of the image. This paper submits a twofold technique to remove this multiplicative speckle noise and to bring a contrast between the object of the interest and the remaining image. First fold includes block based hard (BHT) and soft thresholding (BST) on pixels in wavelet domain where in which the original ultrasound image is divided into Non Overlapping blocks of sizes 8, 16, 32 and 64. The second fold includes restoration of the object boundaries and texture with adaptive wavelet fusion which are lost by the blurring effect caused as a result of the first fold. Fusion of wavelet coefficients of original US image and block thresholded US images assuaged to restore the degraded object. Fusion rule and wavelet decomposition level are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). Visual quality through twofold processing has improved to an interesting level. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.

Bayesian Fusion of Ensemble of Multifocused Noisy Images

This paper addresses the problem of simultaneous fusion and denoising of an ensemble of multifocused noisy source images using statistical approach. The central theme of the paper is to develop a novel generalized Bayesian framework based on maximum a posteriori (MAP) estimation technique to obtain the fused image from the noisy observations using a multiscale wavelet transform. A mathematically tractable multivariate a priori function is used in the MAP estimator to derive the closed-form expression of the fusion rule for the wavelet coefficients of noisy images. Experiments are carried out on a number of test-sets having an ensemble of multifocused source images with varying noise strengths to evaluate the performance of the proposed MAP-based fusion method as compared to the existing methods. Results show that the performance of the proposed method is better than that of the other wavelet or principal component analysis-based methods in terms of various metrics such as the structural similarity, peak signal-to-noise ratio and cross-entropy, uses of which are common both in the areas of fusion and denoising. In addition, the proposed method yields excellent results in terms of visual quality even in the case of non-Gaussian noise as well as computational load.

Implementation of No Reference Distortion Patch Features Image Quality Assessment Algorithm Based on Human Visual System

One of the most popular waveform based coding scheme for images is Transform coding. It is also known that the wavelet coefficients of no reference images possess certain regularities by the image distortions. The NRDPF-IQA (No Reference Distortion Patch Features-Image Quality Assessment) is one of the No-Reference Image Quality algorithms, which is the proposed algorithm in this paper. The proposed algorithm estimates quality based on features of the image. However the previous BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) algorithm operates based on low computational complexity and C-DIIVINE (complex extension of the DIIVINE) algorithm based on complex Gaussian scale mixture model. In this paper, we present NRDPF-IQA algorithm, which blindly assesses image quality based on advanced generalized Gaussian distortion model. All these distortions have characteristic features that are consistent across No-Reference images. We also compare the results with all existing algorithms. The experimental results show that this NRDPF-IQA to yield an improvement to perform the better results compare with other recent No-Reference algorithms.