Undecimated double density dual tree wavelet transform based image denoising using a subband adaptive threshold

In this paper, an undecimated double density dual tree discrete wavelet transform (UDDDT-DWT) based image resolution enhancement technique is proposed. The critically sampled discrete wavelet transform (DWT) suffers from the drawbacks of being shift-variant and lacking the capacity to process directional information in images. The double density dual tree wavelet transform (DDDT-WT) is an approximately shift-invariant transform capturing directional information. The UDDDT-DWT is an improvement of the DDDT-DWT, making it exactly shift-invariant. The method uses a forward and inverse (UDDDT-DWT) to construct a high-resolution (HR) image from the given lower-resolution (LR) image. The HR image is reconstructed from the LR image using the inverse UDDDT-DWT. Results are presented and discussed through comparisons between state-of-the-art resolution enhancement methods.

Face recognition is extensively used to establish the identity of an individual for several modern applications as it is contactless and non-invasive. In this paper, we propose a method for face recognition using modified power law transform and double density dual tree discrete wavelet transform. Various typical face databases are used to test the projected method involving face detection based on viola jones algorithm. The novel concept is introduced to modify the existing power law transform to enhance quality of face region for better recognition. The Double Density Dual Tree Discrete Wavelet Transforms (DDDTDWT) is used to extract features. The Euclidian Distance (ED) is used to match the obtained features of database and test face images to compute performance parameters. It is noticed that the performance of the proposed method is better compared to existing methods.

Image fusion involves combining two or more images to produce a single image with improved visual quality. This paper presents a discrete wavelet transform (DWT) image fusion methodology based on the intensity magnitudes of the wavelet coefficients and compares three variations of the DWT implemented separately in this fusion model. The image fusion model, using the decimated discrete wavelet transform (DDWT), the undecimated discrete wavelet transform (UDWT), and the dual tree discrete wavelet transform (DT DWT), is applied to multi-focus and multi-sensor images. The resulting fused images are compared visually and through root mean square error (RMSE) computations.

Early and accurate detection of asymptomatic emboli is important for monitoring of preventive therapy in stroke-prone patients. One of the problems in detection of emboli is the identification of an embolic signal caused by very small emboli. The amplitude of the embolic signal may be so small that advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells. In this study instead of conventional discrete wavelet transform, the Dual Tree Complex Discrete Wavelet Transform was used for denoising embolic signals. Performances of both approaches were compared. Unlike the conventional discrete wavelet transform discrete complex wavelet transform is a shift invariant transform with limited redundancy. Results demonstrate that the Dual Tree Complex Discrete Wavelet Transform based denoising outperforms conventional discrete wavelet denoising. Approximately 8 dB improvement is obtained by using the Dual Tree Complex Discrete Wavelet Transform compared to the improvement provided by the conventional Discrete Wavelet Transform (less than 5 dB).

In recent years, cone-adapted shearlets have been established as a powerful tool for the detection of directional information in images. It has also turned out that an interpretation of cone-adapted shearlets in the realm of coorbit space theory exists. Then, cone-adapted shearlets are defined by requiring a certain sparsity pattern of the frame expansion coefficients. In this note, we discuss some structural properties of these spaces.

The ideal objective of combining therapeutic pictures is converging of the numerous pictures acquired from multimodalities into a solitary picture for prevalent sickness analysis. A tale calculation for skillful melding of multimodal restorative pictures is proposed in this paper. For combining of restorative pictures, Undecimated Discrete Wavelet Transform domain (UDWT) and Intuitionistic fuzzy sets are used. Dominant of the available fusion techniques are working in light of Discrete Wavelet Transform (DWT). A slight obscuring is seen when DWT is utilized for image fusion. By using UDWT, this blurring is significantly reduced. There is no decimation process in UDWT. Henceforth, wavelet coefficients are processed for every area permitting better identification of prevailing highlights. It is a non-orthogonal multi-resolution decomposition. In the UDWT domain, the low recurrence sub-groups are combined by the Maximum Selection Rule and the high recurrence sub-groups are intertwined by the Modified Spatial Frequency (MSF). Recreations are carried on different therapeutic pictures and diverged from strategies exist up until now. Predominance of the proposed technique is introduced and supported. Combined picture quality is affirmed with number of value measurements i.e., Entropy (H), Spatial Frequency (SF), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Edge-based image fusion metric (QAB/F), Correlation Coefficient (CC), Quality Index (QI) and Structural Similarity (SSIM).

A New Wavelet-based image denoising using undecimated discrete wavelet transform and least squares support vector machine

The double tree algorithm is one of the most expensive algorithms in wavelet image compression. We introduce strategies for implementing this algorithm on parallel MIMD computers. Experimental results concerning the efficiency of the parallelization on a network of workstations (NOW) are included.

A novel selectivity technique to identify the faulty feeder in unearthed Medium Voltage (MV) networks is introduced. The proposed technique is based on a simplified probabilistic method applied to transient features extracted from the residual currents only using the discrete wavelet transform (DWT). DWT will enhance the accuracy of localising fault events with different fault resistances and the probability will give the protection decision (detection/selectivity). The standard deviation of a window of the DWT detail coefficient is used to detect the fault and an adaptive threshold is used as input to the probability function to estimate the faulty feeder. A faulty unearthed 20 kV network is simulated by ATP/EMTP imitating practical fault cases such as arcing and tree leaning faults. The time-domain test cases provide evidence of the efficacy of the proposed technique.

In many ultrasonic imaging applications, the signal acquired is embedded in noise, and situations with very small signal-to-noise ratio (SNR) are not uncommon. Thus, before any data analysis can be applied to the signal some level of noise removal is necessary. In this paper, we analyze the denoising performance of the discrete wavelet transform (DWT), discrete cosine transform (DCT), and Walsh-Hadamard transform (WHT) using an adaptive thresholding function (ATF) that, when applied to DWT, DCT and WHT coefficients, improves the signal-to-noise (SNR) of ultrasonic signals embedded in noise. In particular, the ATF technique is successful in denoising low SNR ultrasonic signals. Furthermore, the ATF approach outperforms the classical techniques when the ultrasonic signal has a low SNR (below 5dB). For signals with uniform noise added to DWT coefficients, the ATF technique achieves SNR improvements around 9dB over the classical thresholding techniques; these improvements are above 10dB for Gaussian noise. �0.5 �0.4 �0.3 �0.2 �0.1 0 0.1 0.2 0.3 0.4 0.5 �0.5 �0.4 �0.3 �0.2 �0.1 0 0.1 0.2 0.3 0.4 0.5

The importance of Satellite Aperture Radar (SAR) imagery systems is increasing day-by-day in various field such as earth observation, hi-technology war mechanisms, etc. The images captured by SAR imagery systems are mainly used to detect and classify objects captured in the images. Due to the complexity of the image capturing process, SAR images can be highly noisy – often consisting of multiplicative noise, also known as speckle. To detect or classify objects in SAR images, speckle noise must be removed from images. During despeckling process, the preservation of important information in the SAR images while removing noise such as edge or patterns is a crucial task. Despeckling methods are liable to compromise on edge preservation ability while aspiring for good quality denoised images. Many researchers have proposed wavelet transform based despeckling approaches such as Discrete Wavelet Transform (DWT), Undecimated Wavelet Transform (UDWT), Dual Tree Complex Wavelet Transform (DTCWT). In these approaches, finding the best values of the coefficients plays an important role in yielding excellent denoised images with preserved edges. In this paper, we have proposed a novel optimization framework that optimizes thresholding coefficients of DTCWT despeckling method for SAR images. The proposed optimization framework is based on Fruit Fly Optimization (FOA) algorithm. The approach is a multi-objective optimization algorithm that is used to find maximum values for Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM) and Equivalent number of look (ENL). The maximum value of MSSIM indicates high edge preservation capacity, whereas maximum PSNR value indicates good quality denoising of images. The maximum ENL value represents a good speckle-noise smoothing capability. We have applied our framework over some classical images as well as over SAR images of MSTAR dataset. In our experiments, we found that our proposed framework results in the excellent PSNR 36.87 dB, 35.4 dB and 37.8 dB, MSSIM values 0.92, 0.93 and 0.92, respectively, in the case of lena image, MSTAR dataset image and TerraSAR-X dataset image.

On the basis of analyzing the performances of popular image fusion methods, a new remote sensing image fusion method based on principal component analysis (PCA), high pass filter (HPF) and undecimated discrete wavelet transform (UDWT) is proposed. Some measure parameters are suggested to evaluate the fusion method. Experiments have been performed with the SPOT panchromatic image and the TM multi-spectral image. Both subjectively qualitative analysis and objectively quantitative evaluation verify the performance of the new method. With the same wavelet transform level, the fusion image using the proposed method preserves more sophisticated spatial details and distorts less spectral information in comparison with the fusion image using the traditional discrete wavelet transform (DWT) method.

This paper presents a new approach for human automatical detection based on ultrasound signal processing. Two signal decomposition techniques, empirical mode decomposition and discrete wavelet transform are used in the presented method. Performance comparison of these two decomposition methods when applied to this specific ultrasound signal is given. Described method includes ultrasonic signal decomposition to enhance signal specific features and increase signal to noise ratio with the following decision rules based on adaptive thresholding. The resulting detection performance of the proposed method using empirical mode decomposition was better to compare to discrete wavelet transform and resulted in 70% correctly identified healthy and 82%, 97% and 100% correctly identified cataract cases in the incipience, immature and mature subject groups, respectively. Discussion is given on the reasons of different results and the differences between the two used signal decomposition techniques.

Digital spectral analysis of Heart Rate Variability (HRV) signals provides quantitative markers of the Autonomic Nervous System (ANS) activity, underpinning a variety of physiologic processes. In this investigation, a robust algorithm was developed to derive HRV signals, their FFT- or AR-based spectra and their standard spectral features from either electrocardiogram (ECG) or photoplethysmographic (PPG) signals. ECG/PPG signals were first interpolated to a common sampling rate and detrended using a first order DC-notch IIR high-pass filter to remove very low frequencies. Next, the undecimated Discrete Wavelet Transform (DWT) Daubechies-6 family of filters was used to selectively remove some of the high-frequency subbands from the signals. The filtered ECG/PPG signals were then squared to increase the dynamic range of the target dominant peaks from which accurate peak-to-peak intervals could be extracted. A smart peak-tracking and monitoring algorithm was used to detect the peaks while enforcing a valid trend of the instantaneous heart rate. Once the peak-to-peak intervals were obtained, the HRV signal was determined using cubic spline interpolation to create a signal at a very low sampling rate of 1-4 Hz. Standard Power Spectrum Estimation (PSD) of the HRV signal was then performed to generate normalized LF, HF and LF/HF spectral features to quantify parasympathetic influences and sympathovagal balance.

In this paper, a comparative study of different wavelet filters using improved sub-band adaptive thresholding function for denoising of satellite images, based on evolutionary algorithms, has been performed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC) and particle swarm optimisation (PSO) are used for obtaining the parameters of adaptive thresholding function required for optimum performance. The visual and quantitative results clearly show the increased efficiency and flexibility of the proposed CS algorithm based on Meyer wavelet filter over various other wavelet filters for image denoising. From the comparative study of different wavelet filters, it is found that the proposed Meyer wavelet-based CS algorithm denoising approach gives better performance in terms of signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE) and mean as compared to ABC- and PSO-based denoising approach. The proposed technique has been tested on several satellite images. The quantitative (EKI or EPI, mean, MSE, SNR and PSNR) and visual (denoised images) results show the superiority of the proposed technique over conventional and state-of-art image denoising techniques.