Wavelet Coefficients Of Image Research Articles

A video, text, and speech-driven realistic 3-d virtual head for human-machine interface.

A multiple inputs-driven realistic facial animation system based on 3-D virtual head for human-machine interface is proposed. The system can be driven independently by video, text, and speech, thus can interact with humans through diverse interfaces. The combination of parameterized model and muscular model is used to obtain a tradeoff between computational efficiency and high realism of 3-D facial animation. The online appearance model is used to track 3-D facial motion from video in the framework of particle filtering, and multiple measurements, i.e., pixel color value of input image and Gabor wavelet coefficient of illumination ratio image, are infused to reduce the influence of lighting and person dependence for the construction of online appearance model. The tri-phone model is used to reduce the computational consumption of visual co-articulation in speech synchronized viseme synthesis without sacrificing any performance. The objective and subjective experiments show that the system is suitable for human-machine interaction.

Decoding and encoding of visual patterns using magnetoencephalographic data represented in manifolds

Visual decoding and encoding are crucial aspects in investigating the representation of visual information in the human brain. This paper proposes a bidirectional model for decoding and encoding of visual stimulus based on manifold representation of the temporal and spatial information extracted from magnetoencephalographic data. In the proposed decoding process, principal component analysis is applied to extract temporal principal components (TPCs) from the visual cortical activity estimated by a beamforming method. The spatial distribution of each TPC is in a high-dimensional space and can be mapped to the corresponding spatiotemporal component (STC) on a low-dimensional manifold. Once the linear mapping between the STC and the wavelet coefficients of the stimulus image is determined, the decoding process can synthesize an image resembling the stimulus image. The encoding process is performed by reversing the mapping or transformation in the decoding model and can predict the spatiotemporal brain activity from a stimulus image. In our experiments using visual stimuli containing eleven combinations of checkerboard patches, the information of spatial layout in the stimulus image was revealed in the embedded manifold. The correlation between the reconstructed and original images was 0.71 and the correlation map between the predicted and original brain activity was highly correlated to the map between the original brain activity for different stimuli (r=0.89). These results suggest that the temporal component is important in visual processing and manifolds can well represent the information related to visual perception.

Analysis and improvement of the dynamic watermarking scheme for quantum images using quantum wavelet transform

We investigate the dynamic watermarking scheme for quantum images using quantum wavelet transform (QWT) proposed by Song et al. (Quantum Inf Process 12(12):3689---3706, 2013). It is aimed to embed the watermark image into the wavelet coefficients of the quantum carrier image. However, in our opinion, the key procedures of the protocol are wrong. At last, a possible improvement strategy is presented.

Image quality/distortion metric based on α-stable model similarity in wavelet domain

Quality assessment is of central importance in numerous image processing tasks. State-of-the-art objective image quality assessment (IQA) algorithms are generally devised for specific distortion types or based on training procedure of large databases. In this work, we propose a general-purpose full-reference/no-reference (FR/NR) IQA framework for image distortions, nominated by Image Quality/Distortion Metric (IQDM). The leptokurtic and heavy-tailed behaviors of image wavelet coefficients are characterized by symmetric α-stable (SαS) density, and the statistical studies indicate that the model parameters may be altered because of the presence of distortion. This important priori knowledge of original image’s distribution is then used to gauge the distortion between degraded and reference SαS models in multi-scale wavelet sub-bands. We investigate the relationship between original and degraded parameters over scales, accordingly infer the original parameters from the degraded ones. A characteristic probability density function for SαS and its closed-form Kullback–Leibler distance are derived for FR/NR-IQDM using the model parameters. Extensive experiments and comparisons demonstrate that the proposed FR/NR-IQDM scheme is efficacious to most common types of distortion, and leads to a highly comparable performance to the benchmarks and prevalent competitors in consistency with subjective judgements.

An Effective Image Coding Method using Lattice Vector Quantization in Wavelet Domain

As the digital information must be stored and recovered in an effective ways, it is necessary to compress data in image coding, wavelet transform and lattice vector quantization are adopted in this paper to improve the compression effective of image coding. We first propose the two dimensional spatial wavelet decomposition of image, and then we present lattice Vector Quantization coding scheme for each image subband, lattice vector quantization can make full use of the correlation between the image wavelet coefficients to remove the information redundancy, and the coding method of lattice quantization can be effective because of applying the symmetries of the lattice. The establishment of rate distortion (RD) model suitable for lattice vector quantization of wavelet image coder is also important for image compression, study shows that the RD performance for the spatial subbands are fitted by an exponential form theoretical model, this yields an analytical solution to the bit rate distribution issue, we explore an effective rate control scheme by using the lagrangian optimization method to distribute the bit rate for the spatial subbands. The experimental results show that the proposed algorithm can achieve better compressing effect with minimum loss.

Microarray image denoising using complex Gaussian scale mixtures of complex wavelets.

Microarray images when contaminated with noise may severely affect the detection and quantification of gene expression. In this paper, we propose to use the complex Gaussian scale mixture (CGSM) model in complex wavelet domain for noise reduction in complementary DNA microarray images. Based on the joint information in the red and green channel microarray images, we model the complex wavelet coefficients of the channel images jointly using the CGSM, and subsequently perform image denoising using Bayes least square estimator in complex wavelet domain. The experimental results show that using the CGSM of complex wavelet coefficients provides better noise reduction of microarray images compared to other complex wavelet-based models.

Determination of Noise Threshold from Signal Histogram in the Wavelet Domain

Thresholding in frequency domain is a simple and effective noise reduction technique. Determination of the threshold is critical to the image quality. The optimal threshold minimizing the Mean Square Error (MSE) is chosen adaptively in the wavelet domain; we utilize an equation of the MSE for the soft-thresholded signal and the histogram of wavelet coefficients of the original image and noisy image. The histogram of the original signal is estimated through the deconvolution assuming that the probability density functions (pdfs) of the original signal and the noise are statistically independent. The proposed method is quite general in that it does not assume any prior for the source pdf.

Stereoscopic Images Enhancement Based on Edge Sharpening of Wavelet Coefficients

This paper proposed a new image enhancement algorithm based on edge sharpening of wavelet coefficients for stereoscopic images. Our scheme uses the multi-scale characteristic of wavelet transform, decomposes the original image into low frequency approximation sub-graph and several high frequency direction. Under the multi-scale, the low frequency approximation sub-graph is processed by edge sharpening method. Then the low frequency sub-graph decomposes in multi-scale again. At last, the low frequency approximation graph after four layers decompose sharpening and the high frequency approximation of the decomposed sub-graph will be refactored to get the new image. Experimental results show that whether PSNR or visual effect, or the subjective assessment of the DMOS value, the proposed method has better enhanced performance than the conventional edge sharpening and wavelet transform. And it has good image edge enhancement, details protection. Meanwhile, the proposed algorithm has the same computational complexity with wavelet transform.

Sonar target enhancement by shrinkage of incoherent wavelet coefficients

Background reverberation can obscure useful features of the target echo response in broadband low-frequency sonar images, adversely affecting detection and classification performance. This paper describes a resolution and phase-preserving means of separating the target response from the background reverberation noise using a coherence-based wavelet shrinkage method proposed recently for de-noising magnetic resonance images. The algorithm weights the image wavelet coefficients in proportion to their coherence between different looks under the assumption that the target response is more coherent than the background. The algorithm is demonstrated successfully on experimental synthetic aperture sonar data from a broadband low-frequency sonar developed for buried object detection.

The benefit of tree sparsity in accelerated MRI

The wavelet coefficients of a 2D natural image are not only approximately sparse with a large number of coefficients tend to be zeros, but also yield a quadtree structure. According to structured sparsity theory, the required measurement bounds for compressive sensing reconstruction can be reduced to O(K+log(N/K)) by exploiting the tree structure rather than O(K+Klog(N/K)) for standard K-sparse data. In this paper, we proposed two algorithms with convex relaxation to solve the tree-based MRI problem. They are based on FISTA and NESTA frameworks respectively to validate the benefit of tree sparsity in accelerated MRI, both of which have very fast convergence rate and low computational cost. These properties make them be comparable with the fastest algorithms with standard sparsity. Extensive experiments are conducted to validate how much benefit it can bring by tree sparsity. Experimental results show that the proposed methods can definitely improve existing MRI algorithms, although with gaps to the theory.

Meta-heuristic algorithms for optimized network flow wavelet-based image coding

Optimal multipath selection to maximize the received multiple description coding (MDCs) in a lossy network model is proposed. Multiple description scalar quantization (MDSQ) has been applied to the wavelet coefficients of a color image to generate the MDCs which are combating transmission loss over lossy networks. In the networks, each received description raises the reconstruction quality of an MDC-coded signal (image, audio or video). In terms of maximizing the received descriptions, a greater number of optimal routings between source and destination must be obtained. The rainbow network flow (RNF) collaborated with effective meta-heuristic algorithms is a good approach to resolve it. Two meta-heuristic algorithms which are genetic algorithm (GA) and particle swarm optimization (PSO) have been utilized to solve the multi-objective optimization routing problem for finding optimal routings each of which is assigned as a distinct color by RNF to maximize the coded descriptions in a network model. By employing a local search based priority encoding method, each individual in GA and particle in PSO is represented as a potential solution. The proposed algorithms are compared with the multipath Dijkstra algorithm (MDA) for both finding optimal paths and providing reliable multimedia communication. The simulations run over various random network topologies and the results show that the PSO algorithm finds optimal routings effectively and maximizes the received MDCs with assistance of RNF, leading to reduce packet loss and increase throughput.

A fast mixing matrix estimation method in the wavelet domain

In the field of blind image separation (BIS) based on the sparse component analysis, separation efficiency and accuracy are directly affected by the number of clustering samples. To address this problem, a new algorithm for the detection of points in the Haar wavelet domain was proposed in which only single source contributions occur. The algorithm identified the single source points (SSPs) by comparing the absolute direction between the diagonal and horizontal components of the Haar wavelet coefficients of mixed images. After screening the SSPs, the wavelet coefficients of the images are sparser. The experimental results showed that, compared to the conventional method, the proposed algorithm could estimate the mixing matrix faster and more accurately, and it allowed identification of latent variables via statistical histograms.

Research on Wavelet-Based Image Target Detection & Recognition Technology

With the development of sensor technology, image fusion becomes increasingly important in complex battlefield environment. As traditional methods for precision-guided weapons systems exist defects of fuzzy detection, low recognition rate and poor real-time; this paper proposes a new fusion algorithm of combining wavelet transform with Canny operator. Firstly, the improved method detects the edge with Canny operator; Then using wavelet transform decomposes images; Next, adapts unique fusion rules to change wavelet coefficients of images, using weighted average algorithm for low-frequency component, while using a local area average variance criterion for high-frequency; Finally, using the inverse wavelet transform to obtain reconstructed images. Theoretical analysis and simulation results show: the improved method not only reduces the fuzziness and highlights target color, but also makes computational efficiency high, real-time good.

An improved wavelet image lossless compression algorithm

The wavelet transform divides image into different but interrelated multi-resolution and multi-level sub-bands, so quantization coding strategy of image wavelet coefficient has great flexibility. To address to energy aggregation and correlated features of image wavelet coefficient, Hilbert and singular value truncating were introduced to wavelet to propose an Improved Wavelet Lossless Compression Algorithm (IWLCA). It mainly performs classification rearrangement on low-frequency sub-band coefficient of wavelet image in accordance with Hilbert curve, but conducts singular value truncating transform on high-frequency coefficient. Then entropy encoding was combined to implement lossless image compression. Experimental results show that IWLCA has high encoding efficiency, which can also effectively reduce encoding bit rate of lossless image compression. Compared with mainstream lossless algorithms as JPEG-LS and JPEG 2000, the compression rate was significantly improved.

Fusion of multimodal medical images using Daubechies complex wavelet transform – A multiresolution approach

Multimodal medical image fusion is an important task for the retrieval of complementary information from medical images. Shift sensitivity, lack of phase information and poor directionality of real valued wavelet transforms motivated us to use complex wavelet transform for fusion. We have used Daubechies complex wavelet transform (DCxWT) for image fusion which is approximately shift invariant and provides phase information. In the present work, we have proposed a new multimodal medical image fusion using DCxWT at multiple levels which is based on multiresolution principle. The proposed method fuses the complex wavelet coefficients of source images using maximum selection rule. Experiments have been performed over three different sets of multimodal medical images. The proposed fusion method is visually and quantitatively compared with wavelet domain (Dual tree complex wavelet transform (DTCWT), Lifting wavelet transform (LWT), Multiwavelet transform (MWT), Stationary wavelet transform (SWT)) and spatial domain (Principal component analysis (PCA), linear and sharp) image fusion methods. The proposed method is further compared with Contourlet transform (CT) and Nonsubsampled contourlet transform (NSCT) based image fusion methods. For comparison of the proposed method, we have used five fusion metrics, namely entropy, edge strength, standard deviation, fusion factor and fusion symmetry. Comparison results prove that performance of the proposed fusion method is better than any of the above existing fusion methods. Robustness of the proposed method is tested against Gaussian, salt & pepper and speckle noise and the plots of fusion metrics for different noise cases established the superiority of the proposed fusion method.

Multivariate pursuit image reconstruction using prior information beyond sparsity

The prior information of images plays an important role in reducing the computational complexity of CS inversion and improving the reconstruction quality. A wavelet-based multivariate pursuit algorithm, which exploits the prior information of images that goes beyond simple sparsity, is developed in this paper. The proposed method reconstructs the image wavelet coefficients from the multiple measurements in a multivariate manner, and uses the extracted image edge as the prior information to guide the pursuit process of algorithm in CS recovery. By means of the interaction of edge information and multivariate joint recovery, the proposed algorithm significantly improves the reconstruction quality of those images with the obvious edges and high sparsity, such as CT, MRI images. Numerical experiments demonstrate that the proposed algorithm returns superior reconstructed quality and remains higher computational efficiency than other state-of-the-art CS algorithms.

Robust blind image watermarking scheme based on Redundant Discrete Wavelet Transform and Singular Value Decomposition

Copyright protection and proof of ownership are two of the main important applications of the digital image watermarking. The challenges faced by researchers interested in digital image watermarking applications lie in the creation of new algorithms to serve those applications and to be resistant to most types of attacks, especially the geometrical attacks. Robustness, high imperceptibility, security, and large capacity are four essential requirements in any watermarking scheme. This paper presents a new image watermarking scheme based on the Redundant Discrete Wavelet Transform (RDWT) and the Singular Value Decomposition (SVD). The gray scale image watermark was embedded directly in the singular values of the RDWT sub-bands of the host image. The scheme achieved a large capacity due to the redundancy in the RDWT domain and at the same time preserved high imperceptibility due to SVD properties. Embedding the watermarking pixel's values without any modification inside the wavelet coefficient of the host image overcomes the security issue. Furthermore, the experimental results of the proposed scheme showed a high level of robustness not only against the image processing attacks but also against the geometrical attacks which are considered as difficult attacks to resist.

Integer Wavelet based Secret Data Hiding by Selecting Variable Bit Length

is a process of hiding secret informations like image, video, audio, and text message in a carriers like image, video, and audio. Proposed method uses the secret information as a text message which is embedded in a color image. The existing methods hide the information using constant bit length in integer wavelet coefficients. This paper uses variable bit length based on integer wavelet coefficients to hide the data in a particular positions using secret key by LSB substitution method. Hence this algorithm increases the embedding capacity of the text message and obtained stego image is imperceptible for human vision. This technique embeds binary bit stream of secret text message into the 8 bit color image. The embedded confidential information can be extracted from stego image without the knowledge of original image by considering the same secret key. On average of 2.50 bits can be embedded in each wavelet coefficient of the input color image. Hence this method gives better results in both embedding capacity and PSNR than the existing methods.

SU‐E‐I‐115: Wavelet Analysis of Ultrasound Image for the Diagnosis of Sjögren's Syndrome

Sjögren's syndrome (SS) is an auto-immune disease presenting with dry eyes and mouth (keratoconjunctivitis sicca and xerostomia). Ultrasonography is used for the initial and non-invasive investigation of the parotid gland in the disease. The purpose of this study is to develop an image processing for diagnosis of SS by applying wavelet analysis to ultrasound image. Ultrasound B-mode images of the parotid gland were captured and analyzed by a personal computer. A square region of interest (ROI) was set on the image and two-dimensional discrete wavelet transform was performed within the ROI. As a Result, the image was decomposed into an approximate image and three detailed images in vertical, horizontal and diagonal directions in different scales. A feature quantity for image classification was defined by calculating from the wavelet coefficients of detailed images within selected scales. The ultrasound images of 80 patients who had been referred to Nagasaki University Hospital because of suspicion of SS were analyzed. A total of 37 patients fulfilled the criteria for SS, whereas the remaining 43 patients did not. The severity of SS was graded into four degrees by sialography. The images with each feature quantity were classified by statistical cluster analysis. In this method, the images can be divided into two groups which mainly contained SS and non-SS. The sensitivity and specificity in the detection of SS was 78% and 95%, respectively. It was also found that the defined feature quantity tended to change with the severity of SS. In ultrasonography, the image analysis based on wavelet transform was useful for the diagnosis of Sjögren's syndrome.

Image resolution enhancement using statistical estimation in wavelet domain

The goal of medical image resolution enhancement is to reconstruct a higher-resolution image from its lower-resolution counterpart. This paper proposes a Bayesian approach in the wavelet domain by exploiting a Bayesian inference framework to mathematically formulate the image interpolation problem. Furthermore, the proposed approach jointly estimates both the unknown wavelet coefficients of the high-resolution image and the unknown parameters of the statistical model for wavelet coefficients. Experiments are conducted to demonstrate the superior performance of the proposed approach.