Discrete Stationary Wavelet Transform Research Articles

Medical image fusion via discrete stationary wavelet transform and an enhanced radial basis function neural network

Medical image fusion of images obtained via different modes can expand the inherent information of original images, whereby the fused image has a superior ability to display details than the original sub-images, to facilitate diagnosis and treatment selection. In medical image fusion, an inherent challenge is to effectively combine the most useful information and image details without information loss. Despite the many methods that have been proposed, the effective retention and presentation of information proves challenging. Therefore, we proposed and evaluated a novel image fusion method based on the discrete stationary wavelet transform (DSWT) and radial basis function neural network (RBFNN). First, we analyze the details or feature information of two images to be processed by DSWT by using two-level decomposition to separate each image into seven parts, comprising both high-frequency and low-frequency sub-bands. Considering the gradient and energy attributes of the target, we substituted the pending parts in the same position in the two images by using the proposed enhanced RBFNN. The input, hidden, and output layers of the neural network comprised 8, 40, and 1 neuron(s), respectively. From the seven neural networks, we obtained seven fused parts. Finally, through inverse wavelet transform, we obtained the final fused image. For the neural network training method, the hybrid adaptive gradient descent algorithm (AGDA) and gravitational search algorithm (GSA) were implemented. The final experimental results revealed that the novel method has significantly better performance than the current state-of-the-art methods.

Combining Gabor energy with equilibrium optimizer algorithm for multi-modality medical image fusion

Medical image fusion is a technique of extracting information from multiple image modalities and combining them to create a single image with the aim of improving the image content and preserving information. Until now, many approaches have been introduced to enhance efficiency in medical image fusion. Nevertheless, there are still limitations to the fusion of the low and high-frequency coefficients of several current methods to our current knowledge. The first limitation is that the output images often lose detailed information because high-frequency coefficients can be fused by ineffective rules. The second limitation is the deterioration of the resulting image luminance when the low-frequency coefficients are fused by the average rule. In this study, we propose a novel method to overcome the limitations mentioned above, and this approach is described by several steps as follows. Firstly, the discrete stationary wavelet transform (DSWT) method is used to convert input images into high and low-frequency components. Secondly, a rule based on maximum Gabor energy (MGE) is introduced to fusing high-frequency coefficients, which allows important information to be preserved in the resulting image. Thirdly, low-frequency coefficients are fused by optimal parameters based on the equilibrium optimizer algorithm (EOA). This fusion rule ensures the resulting image has good quality. In order to verify our approach's effectiveness, we have used six image quality indexes and the latest five medical image fusion methods for comparison. The experimental results show that the proposed approach has overcome the disadvantages of some current methods.

An Improved Method for NURBS Free-Form Surface Based on Discrete Stationary Wavelet Transform

In order to improve fitting accuracy of free-form surface, an improved method for the currently recognized NURBS free-form surface based on discrete stationary wavelet transform is proposed. Firstly, the elevation image of the free-form surface point cloud and the elevation sequence of the elevation image are obtained by using the grid method. Then, feature points of the free-form surface are extracted by the discrete stationary wavelet transform. Finally, using the extracted feature points, high accuracy NURBS free-form surface is realized. Compared with the conventional NURBS method and the NURBS method based on dual-tree complex wavelet transform, the experimental results showed that the root mean squared errors of fitting results of free-form surface were deduced 77.64% and 23.16%, respectively. Therefore, the proposed method has satisfactory fitting precision.

Accounting for SNR in an Algorithm Using Wavelet Transform to Remove ECG Interference from EMG Signals

When the electromyography (EMG) signal is acquired from muscles in the torso, the electrocardiography (ECG) signal coming from heart activity can interfere. As a result, the EMG signal can be contaminated during data collection. In this paper, a technique based on discrete stationary wavelet transform (DSWT) is proposed to remove ECG interference from the EMG signal while taking into account the signal-to-noise ratio (SNR). The contaminated EMG signal is decomposed using 5-level DSWT with the Symlet wavelet function. The coefficients for levels 4 and 5, which are contaminated by ECG, are set to zero when their absolute values are less than or equal to a threshold determined for each SNR level. A clean EMG signal can then be obtained by inverse DSWT mapping of the new thresholded coefficients. We evaluated the performance of the proposed algorithm using simulated EMG contaminated with both simulated and real ECG signals, at 9 SNR levels from [Formula: see text]20 to 20[Formula: see text]dB with 5[Formula: see text]dB increments. The performance based on mean absolute error, correlation coefficient and relative error shows that the DSWT method is better than a high-pass filter.

Security of 3D-HEVC transmission based on fusion and watermarking techniques

This paper presents two robust hybrid watermarking techniques for efficiently securing the transmission of the Three-Dimensional High Efficiency Video Coding (3D-HEVC). The first watermarking technique is the homomorphic-transform-based Singular Value Decomposition (SVD) in Discrete Wavelet Transform (DWT) domain. The second technique is the three-level Discrete Stationary Wavelet Transform (DSWT) in Discrete Cosine Transform (DCT) domain. The objective of the two proposed hybrid watermarking techniques is to increase the immunity of the watermarked 3D-HEVC to attacks and achieve adequate perceptual quality. Also, we propose a wavelet-based fusion technique to combine multiple depth watermark frames into one fused depth watermark frame. Then, the resultant fused depth watermark is embedded in the 3D-HEVC color frames using the two proposed watermarking techniques to produce the watermarked 3D-HEVC streams. The proposed techniques reduce the required bit rate for transmitting the color-plus-depth 3D-HEVC data. The performances of the two proposed watermarking techniques are compared with those of the state-of-the-art watermarking techniques. The comparisons depend on both subjective visual results and objective results: the Peak Signal-to-Noise Ratios (PSNRs) of the watermarked frames and the Normalized Correlation (NC) of the extracted watermark frames. Extensive simulation experiments on different standard 3D video sequences have been conducted in the presence of attacks. The obtained results confirm that the proposed watermarking techniques achieve not only very good perceptual quality, appreciated PSNR values, and saving of the transmission bit rate, but also high correlation coefficients, and high robustness in the presence of attacks. Furthermore, the proposed techniques improve both the capacity of the embedded information and the robustness without affecting the perceptual quality of the original 3D-HEVC frames. Indeed, the extraction of the fused and multiple primary and secondary depth watermark frames is possible in the presence of different attacks.

Saliency Detection Based on the Combination of High-Level Knowledge and Low-Level Cues in Foggy Images

A key issue in saliency detection of the foggy images in the wild for human tracking is how to effectively define the less obvious salient objects, and the leading cause is that the contrast and resolution is reduced by the light scattering through fog particles. In this paper, to suppress the interference of the fog and acquire boundaries of salient objects more precisely, we present a novel saliency detection method for human tracking in the wild. In our method, a combination of object contour detection and salient object detection is introduced. The proposed model can not only maintain the object edge more precisely via object contour detection, but also ensure the integrity of salient objects, and finally obtain accurate saliency maps of objects. Firstly, the input image is transformed into HSV color space, and the amplitude spectrum (AS) of each color channel is adjusted to obtain the frequency domain (FD) saliency map. Then, the contrast of the local-global superpixel is calculated, and the saliency map of the spatial domain (SD) is obtained. We use Discrete Stationary Wavelet Transform (DSWT) to fuse the cues of the FD and SD. Finally, a fully convolutional encoder–decoder model is utilized to refine the contour of the salient objects. Experimental results demonstrate that the presented model can remove the influence of fog efficiently, and the performance is better than 16 state-of-the-art saliency models.

Efficient multi-level security for robust 3D color-plus-depth HEVC

This paper presents two robust hybrid watermarking techniques for securing the Three-Dimensional High Efficiency Video Coding (3D-HEVC). The first watermarking technique is the homomorphic-transform-based Singular Value Decomposition (SVD) in Discrete Wavelet Transform (DWT) domain. The second watermarking technique is the three-level Discrete Stationary Wavelet Transform (DSWT) in Discrete Cosine Transform (DCT) domain. The objective of the two proposed hybrid watermarking techniques is to increase the immunity of the watermarked 3D-HEVC streams to attacks. Also, we propose a wavelet-based fusion technique to combine two depth watermark frames into one fused depth watermark frame. Then, the resultant fused depth watermark is encrypted using chaotic Baker map to increase the level of security. After that, the resultant chaotic encrypted fused depth watermark is embedded in the 3D-HEVC color frames using the proposed hybrid watermarking techniques to produce the watermarked 3D-HEVC streams. In addition to achieving multi-level security in the transmitted 3D-HEVC streams, the proposed hybrid techniques reduce the required bit rate for transmitting the color-plus-depth 3D-HEVC data over limited-bandwidth networks. The performance of the proposed hybrid techniques is compared with those of the state-of-the-art techniques. Extensive simulation results on standard 3D video sequences have been conducted in the presence of attacks. The obtained results confirm that the proposed hybrid fusion-encryption-watermarking techniques achieve not only a good perceptual quality with high Peak Signal-to-Noise Ratio (PSNR) values and less bit rate, but also high correlation coefficient values between the original and extracted watermarks in the presence of attacks. Furthermore, the proposed hybrid techniques improve the capacity of information embedding and the robustness without affecting the perceptual quality of the original 3D-HEVC frames. Indeed, the extraction of the encrypted, fused, primary, and secondary depth watermark frames is possible in the presence of attacks.

Enhancing manual P-phase arrival detection and automatic onset time picking in a noisy microseismic data in underground mines

Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform (DSWT) and higher order statistics is proposed for processing noisy data from underground mines. The objectives of this method are to (i) improve manual detection and picking of P-phase onset; and (ii) provide an automatic means of detecting and picking P-phase onset time accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average (STA/LTA) ratio and that by Reference Ge et al. (2009). The results show that the method provides a more reliable estimate of the P-phase onset arrival than the STA/LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. (2009).

Robust hybrid watermarking techniques for different color imaging systems

Digital watermarking is an efficient and promising mechanism for protecting the copyright of the transmitted multimedia information. Thus, this paper presents two robust hybrid color image watermarking techniques. The objective of the proposed watermarking techniques is to increase the immunity of the watermarked color images against attacks and to achieve adequate perceptual quality. The first proposed hybrid technique is the homomorphic transform based Singular Value Decomposition (SVD) in Discrete Wavelet Transform (DWT) domain. Firstly, the DWT is employed to divide an image into non-overlapping bands. Then, the reflectance components of the LL sub-bands are extracted using the homomorphic transform of each of the RGB (Red, Green, and Blue) color image components. After that, the watermark is embedded by applying the SVD on these reflectance components. The second proposed hybrid technique is the three-level Discrete Stationary Wavelet Transform (DSWT) in Discrete Cosine Transform (DCT) domain. In this technique, the RGB components of the host color image are separated, and then the DCT is applied on each separated color component. The three-level DSWT is employed to divide the DCT components into four sub-bands. These sub-bands are the A, H, V, and D matrices, which have the same host image size. The watermark image is then embedded into the determined matrix A. The two proposed hybrid watermarking techniques are compared with the current state-of-the-art techniques. This paper also presents a comparative study of the proposed techniques for different color imaging systems to determine their robustness and stability. The comparisons are based on the subjective visual results to detect any degradation in the watermarked image in addition to the objective results of the Peak Signal-to-Noise Ratio (PSNR) of the watermarked image, and the Normalized Correlation (NC) of the extracted watermark to test and evaluate the performance efficiency of the proposed watermarking techniques. Extensive experimental results show that the proposed hybrid watermarking techniques are both robust and have adequate immunity against different types of attacks compared to the traditional watermarking techniques. They achieve not only very good perceptual quality with appreciated PSNR values, but also high correlation coefficient values in the presence of different multimedia attacks.

Discrete stationary wavelet transform based saliency information fusion from frequency and spatial domain in low contrast images

Due to degraded visibility and low signal-to-noise ratio properties, traditional saliency detection models face great challenges toward low contrast images. In this circumstance, it is difficult to extract effective visual features to describe saliency information. To cope with this problem, this paper proposes a salient object detection model utilizing efficient features both from frequency domain and spatial domain in low contrast images. The discrete stationary wavelet transform (DSWT) is used to fuse the saliency information from frequency and spatial domain. The input image is firstly converted into HSV color space, where each color channel is transformed into frequency domain to adjust the amplitude spectrum by a median filter. Then, a superpixel-level feature extraction is utilized to generate saliency map from both local and global spatial information. Finally, the frequency and spatial domain saliency maps are fused via DSWT to obtain the final result. Experiments are carried out on three public datasets containing visible light condition and our low contrast image dataset to demonstrate the effectiveness of the proposed saliency detection model over other ten state-of-the-art saliency models.

Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain

In order to promote the performance of infrared and visual image fusion and provide better visual effects, this paper proposes a hybrid fusion method for infrared and visual image by the combination of discrete stationary wavelet transform (DSWT), discrete cosine transform (DCT) and local spatial frequency (LSF). The proposed method has three key processing steps. Firstly, DSWT is employed to decompose the important features of the source image into a series of sub-images with different levels and spatial frequencies. Secondly, DCT is used to separate the significant details of the sub-images according to the energy of different frequencies. Thirdly, LSF is applied to enhance the regional features of DCT coefficients, and it can be helpful and useful for image feature extraction. Some frequently-used image fusion methods and evaluation metrics are employed to evaluate the validity of the proposed method. The experiments indicate that the proposed method can achieve good fusion effect, and it is more efficient than other conventional image fusion methods.

Non-intrusive Forensic Detection Method Using DSWT with Reduced Feature Set for Copy-Move Image Tampering

The key intention of non-intrusive image forensic detection is to resolve whether an image is original or tampered. In contrast to intrusive methods, there is no supporting pattern that has been embedded into an image to ensure image authenticity. The only accessible cue is the original characteristics of an image. Various non-intrusive techniques have been proposed to ensure image authenticity but no adequate solution exists so far. This article introduced a robust technique by means of Discrete Stationary Wavelet Transform along with Multi Dimension Scaling to detect familiar category of copy-move image tampering. Experimental outcomes shows that proposed technique decreases computational complexity by reducing feature dimension and locate the tampered region more accurately even when the tampered image is blurred, brightness altered, colour reduced and pasted in multiple locations. Overall tamper detection accuracy is greater than 97% and false positive rate close to zero, which indicates that proposed technique will discover tampered region more precisely compared with existing methods.

Multiscale Spectral Methods for Data Indexing in a Radon Space

Abstract This paper presents three methods for multiscale indexing of the content of projection data of computed tomography images in the CBIR-systems for medical database search. The feature spaces in the developed algorithms have been created by means of, respectively: Discrete Stationary Wavelet Transform (DSWT), Shearlet Transform (ST) and Repagulum Wavelet Transform (RWT). A comparative analysis and assessment of the proposed algorithms have been carried out based on experimental studies with computed tomography images.

An Efficient Image Denoising Method for Wireless Multimedia Sensor Networks Based on DT-CWT

Wireless multimedia sensor network (WMSN) is a developed technology of wireless sensor networks and includes a set of nodes equipped with cameras and other sensors to detect ambient environment and produce multimedia data content. In this context, many types of noises occur due to sensors problems, change of illumination, fog, rain, and other weather conditions. These noises usually degrade the digital images acquired by camera sensors. Image denoising in spatial domain is more difficult and time-consuming for real-time processing of WMSNs applications. In this study, an efficient method based on Dual-Tree Complex Wavelet Transform (DT-CWT) is developed to enhance the image denosing in WMSNs. This method is designed to reduce the image noises by selecting an optimal threshold value estimated from the approximation of wavelet coefficients. In our experiment, the proposed method was tested and compared with standard Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) on a set of natural scene images. Better results were achieved by using the DT-CWT in terms of image quality metrics and processing time.

Pixel based Medical Image Fusion Techniques using Discrete Wavelet Transform and Stationary Wavelet Transform

Background: Process of combining significant information from 2 or more images to get an output without any artifact or information loss. Also to perceive all information required for accurate diagnosis and to get high spatial resolution with functional and anatomical information. Methods: DWT and SWT are used with biorthogonal 2.6 and db1. Average fusion rule is applied for fusing low frequency coefficients and for high frequency coefficients region energy rule is applied. Results: Eight sets of real time medical images are used for the analysis. Comparing the fusion of SWT with DWT, Stationary wavelet transform method performs well than the Discrete wavelet transform as the information loss occurs due to down sampling in each of the DWT sub bands which caused in the relevant sub bands are minimized by SWT. Application: Easy to diagnose for a physician in the field of biomedical.

Secured Authentication through Integration of Gait and Footprint for Human Identification

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

Efficient Multimodal Biometric System Based on Feature Level Fusion of Palmprint and Finger Vein

Biometric authentication is playing a vital role in providing security and privacy. This paper presents a contemporary approach for identifying an individual using the multimodal biometrics has great demands to overcome the issues involved in single trait system. Finger vein and palmprint biometric is a promising technology, now-a-days widely used because of its important features such as resistant to criminal tampering, high accuracy, ease of feature extraction and fast authentication speed. The features extracted from the preprocessed finger vein and palmprint images using Contourlet Transform reduce the overall dimensionality and computational complexity. Fusion at feature level utilizes the fused feature vectors of finger vein and palmprint using the Discrete Stationary Wavelet Transform (DSWT) to improve the overall performance of the multimodal biometric system. Integrating the biometric traits increases the robustness of the person identification and reduces fraudulent access. Experimental results based on homologous database demonstrate that the proposed system is very efficient to reduce the False Rejection Rate (FRR) and False Acceptance Rate (FAR).

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

Improving the spectral resolution for closely spaced targets based on MUSIC algorithm

Multiple signal classification (MUSIC) is a high-resolution method in microwave imaging which is severely based on the number of singular values of a multistatic response matrix. However, the role of noise and multiple scattering is crucial to determine the true dimension of the signal subspace. In this article, we show that in the presence of multiple scattering between the targets, some singular values of the signal subspace will greatly be decreased which is a critical point when the noise is added to the system. In this case, these signal singular values will mix with the noise ones and the most popular MUSIC methods degrade and even fail to estimate closely spaced target locations. Here, we introduce a method to solve the ambiguity in determining the number of targets employing a pre-processing method, discrete stationary wavelet transform (DSWT). The DSWT is developed on the basis of features of the reflection coefficient between targets. The application of this algorithm to measurement data shows its superiority over another thresholding-based algorithm, empirical mode decomposition. The results indicate that DSWT+MUSIC yields accurate estimate of the target locations, even in the presence of considerable noise and multiple scattering in the received signal.

Discrete Stationary Wavelet Transform of Array CGH Data for Biomarkers Identification of Hepatocellular Carcinoma

Discrete Stationary Wavelet Transform of Array CGH Data for Biomarkers Identification of Hepatocellular Carcinoma