Double-density Dual-tree Complex Wavelet Transform Research Articles

Single-channel Speech Separation Based on Double-density Dual-tree CWT and SNMF

Speech is essential to human communication; therefore, distinguishing it from noise is crucial. Speech separation becomes challenging in real-world circumstances with background noise and overlapping speech. Moreover, the speech separation using short-term Fourier transform (STFT) and discrete wavelet transform (DWT) addresses time and frequency resolution and time-variation issues, respectively. To solve the above issues, a new speech separation technique is presented based on the double-density dual-tree complex wavelet transform (DDDTCWT) and sparse non-negative matrix factorization (SNMF). The signal is separated into high-pass and low-pass frequency components using DDDTCWT wavelet decomposition. For this analysis, we only considered the low-pass frequency components and zeroed out the high-pass ones. Subsequently, the STFT is then applied to each sub-band signal to generate a complex spectrogram. Therefore, we have used SNMF to factorize the joint form of magnitude and the absolute value of real and imaginary (RI) components that decompose the basis and weight matrices. Most researchers enhance the magnitude spectra only, ignore the phase spectra, and estimate the separated speech using noisy phase. As a result, some noise components are present in the estimated speech results. We are dealing with the signal's magnitude as well as the RI components and estimating the phase of the RI parts. Finally, separated speech signals can be achieved using the inverse STFT (ISTFT) and the inverse DDDTCWT (IDDDTCWT). Separation performance is improved for estimating the phase component and the shift-invariant, better direction selectivity, and scheme freedom properties of DDDTCWT. The speech separation efficiency of the proposed algorithm outperforms performance by 6.53–8.17 dB SDR gain, 7.37-9.87 dB SAR gain, and 14.92–17.21 dB SIR gain compared to the NMF method with masking on the TIMIT dataset.

Brain tumor segmentation using double density dual tree complex wavelet transform combined with convolutional neural network and genetic algorithm

<span>Image segmentation is often faced by low contrast, bad boundaries, and inhomogeneity that made it difficult to separate normal and abnormal tissue. Therefore, it takes long periodto read and diagnose brain tumor patients. The aim of this study was to applied hybrid methods to optimize segmentation process of magnetic resonance image of brain. In this study, we divide the brain tumor images with double density dual-tree complex wavelet transform (DDDTCWT), continued by convolutional neural network (CNN), and optimized by genetic algorithm (GA) with 48 combinations yielding excellent results. The F-1 score was 99.42%, with 913 images test data. The training images consist of 1397 normal MRI images and 302 tumor magnetic resonance imaging (MRI) images resized by 32 x32 pixels. The DDDTCWT transforms the input images into more detail than ordinary wavelet transforms, and the CNNs will recognize the pattern of the output images. Additionally, we applied the GA to optimize the weights and biases from the first layer of the CNNs layers. The parameters used for evaluating were dice similarity coefficient (DSC), positive present value (PPV), sensitivity, and accuracy. The result showed that the combination of DDDTCWT, CNN, and GA could be used to brain MRI images and it generated parameters value more that 95%.</span>

CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells

The quantification of corneal endothelial cell (CEC) morphology using manual and semi-automatic software enables an objective assessment of corneal endothelial pathology. However, the procedure is tedious, subjective, and not widely applied in clinical practice. We have developed the CellsDeepNet system to automatically segment and analyse the CEC morphology. The CellsDeepNet system uses Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast of the CEC images and reduce the effects of non-uniform image illumination, 2D Double-Density Dual-Tree Complex Wavelet Transform (2DDD-TCWT) to reduce noise, Butterworth Bandpass filter to enhance the CEC edges, and moving average filter to adjust for brightness level. An improved version of U-Net was used to detect the boundaries of the CECs, regardless of the CEC size. CEC morphology was measured as mean cell density (MCD, cell/mm2), mean cell area (MCA, μm2), mean cell perimeter (MCP, μm), polymegathism (coefficient of CEC size variation), and pleomorphism (percentage of hexagonality coefficient). The CellsDeepNet system correlated highly significantly with the manual estimations for MCD (r = 0.94), MCA (r = 0.99), MCP (r = 0.99), polymegathism (r = 0.92), and pleomorphism (r = 0.86), with p < 0.0001 for all the extracted clinical features. The Bland–Altman plots showed excellent agreement. The percentage difference between the manual and automated estimations was superior for the CellsDeepNet system compared to the CEAS system and other state-of-the-art CEC segmentation systems on three large and challenging corneal endothelium image datasets captured using two different ophthalmic devices.

Analysis of lifting scheme based Double Density Dual-Tree Complex Wavelet Transform for de-noising medical images

Medical images play a vital role in diagnosis of various diseases. This has paved a path to the extensive use of CT, mammogram, MRI and ultrasound images in the recent days which has caused a rising concern about the radiation dosage that is involved in medical screening process. Owing to this concern low dose screening is widely being performed and has resulted in the introduction of noise, artifacts thus producing low image quality which can adversely affect the judgment of the radiologists. This in turn has led to the demand of enhanced image de-noising techniques. This work is an approach to remove multiple types of noises from low dose medical images using lifting based Double Density Dual-Tree Complex Wavelet Transform (DDDTCWT) and a modified Bernoulli based thresholding technique enhanced by fuzzy optimization technique. The parameters observed from the simulation results of the proposed method were compared with the existing de-noising techniques and results of the proposed method have shown significant improvement over the conventional techniques. The proposed work not only efficiently de-noises the image but also enhances its visual appearance. The Lifting scheme used provides augmented memory for decomposition, thus speeding up the entire de-noising process.

An Efficient Pass Parallel SPIHT based Image Compression using Double Density Dual Tree Complex Wavelet Transform for WSN

For the past two decades, wavelet based image compression algorithms for Wireless Sensor Network (WSN) has gained broad attention than that of the spatial based image compression algorithms. In that, Dual Tree Complex Wavelet Transforms (DTCWT) has provided better results in terms of image quality and high compression rate. However, the selection of DTCWT based image compressions for various WSN based applications is not practically suitable, due to the major limitations of WSN such as, low bandwidth, low energy consumption and storage space. Therefore, an attempt has been made in this paper to develop image compression through simulation by considering the modified block based pass parallel Set Partitioning In Hierarchical Trees (SPIHT) with Double Density Dual Tree Complex Wavelet Transform (DDDTCWT) for compressing the WSN based images. In addition, bivariate shrink method is also adopted with the DDDTCWT to obtain better image quality within less computation time. It is observed through simulation results that above mentioned proposed technique provides better performance than that of existing compression technique

Restoration algorithm for noisy complex illumination

Although promising results have been achieved in the restoration of complex illumination images with the Retinex algorithm, there are still some drawbacks in the processing of Retinex. Considering the noise characteristics of complex illumination images, in this study, we propose a novel restoration algorithm for noisy complex illumination, which combines guided adaptive multi‐scale Retinex (GAMSR) and improvement BayesShrink threshold filtering (IBTF) based on double‐density dual‐tree complex wavelet transform (DDDTCWT) domain. Extensive restoration experiments are conducted on three typical types images and the same image with different noises. On the basis of a series of evaluation indexes, we compare our method to those of state‐of‐the‐art algorithms. The results show that (i) SSIM of the proposed IBTF is superior to traditional Bayes threshold method by 15% as the standard variance is 100. (ii) PSNR of the proposed GAMSR enhances 15% to traditional MSR. (iii) The clarity of final results for restoration speeds up three times than that of original images, and the information entropy is improved slightly too. Therefore, the proposed method can effectively enhance the details, edges and textures of the image under complex illumination and noises.

Automated detection of diabetic foot with and without neuropathy using double density-dual tree-complex wavelet transform on foot thermograms

Diabetic foot is the most common problem among diabetic patients, mainly due to peripheral vascular and neuropathy induced capillary perfusion changes. These pathogenic factors cause superficial temperature changes that can be qualitatively and visually documented using infrared thermography (IRT). Hence, IRT can potentially be used to evaluate the diabetic foot. However, it is tedious to manually interpret these subtle temperature variations by inspecting the feet thermal image. Therefore, an automated system to detect diabetic foot with and without neuropathy is proposed. In this study, 51 healthy individuals and 66 diabetic patients (33 with and 33 without neuropathy) are considered. The segmented plantar foot thermograms are decomposed into coefficients using double density-dual tree-complex wavelet transform (DD-DT-CWT). Several entropy and texture features are extracted from the decomposed images of left, right and bilateral foot. These features are reduced using various dimensionality reduction techniques and subsequently ranked using F-values. The ranked features are fed individually into the different classifiers one by one. The developed system yielded 93.16% accuracy, 90.91% sensitivity and 98.04% specificity using only four locality sensitive discriminant analysis (LSDA) features obtained from bilateral foot thermal images with k-nearest neighbour (kNN) classifier. This automated diabetic foot detection system can be introduced in polyclinics and hospitals to clinically support the clinicians to confirm their manual diabetic foot diagnosis.

Noisy signal filtration using complex wavelet basis sets

Methods of noisy signal filtration using a discrete wavelet transform (DWT) with real basis sets of the Daubechies family are compared to methods employing a double-density dual-tree complex wavelet transform (DDCWT) with excess (nonorthonormalized) basis sets. Recommendations concerning the choice of filter parameters for minimization of the error of noisy signal filtration are formulated.

Speech signal filtration using double-density dual-tree complex wavelet transform

We consider the task of increasing the quality of speech signal cleaning from additive noise by means of double-density dual-tree complex wavelet transform (DDCWT) as compared to the standard method of wavelet filtration based on a multiscale analysis using discrete wavelet transform (DWT) with real basis set functions such as Daubechies wavelets. It is shown that the use of DDCWT instead of DWT provides a significant increase in the mean opinion score (MOS) rating at a high additive noise and makes it possible to reduce the number of expansion levels for the subsequent correction of wavelet coefficients.

Multisource Images Adaptive Fusion Based on Double Density Dual-tree Complex Wavelet Transform

Double density dual-tree complex wavelet transform (DD-DTCWT) as multiscale decomposition tool provides multiresolution and multidirection expansion for images. Compared with the discrete wavelet transform (DWT), it is shift-invariant and can overcome the detail blur and pseudo-Gibbs phenomena. Fusion rule is the key that influences the quality of image fusion. This paper propose a fusion rule based on improved pixel weighted average(IPWA) in approximate images and coefficient absolute value with neighborhood average energy consistency selection(ANECS) in detail images. The proposed fusion rule is associated with DD-DTCWT to make full use of the characteristics of them. Composite coefficients is reconstructed by double-density dual-tree complex wavelet inverse transform (DD-DTCWIT). The performance of the proposed method in this paper is compared to a few existing techniques in the literature. The experiment results demonstrate that the proposed method performs better than those in the literature. In addition, it also reveals that the proposed technique is more robust than those in the literature.

An efficient illumination invariant face recognition framework via illumination enhancement and DD-DT[formula omitted]WT filtering

In this paper, it is shown that multiscale analysis of facial structure and features of face images leads to superior recognition rates for images under varying illumination. The proposed method, which is computationally cost effective, significantly suppresses illumination effects. The problem is defined as how better to extract the reflectance portion from a given image. This can be used directly as an input to a dimensionality reduction unit followed by a classifier for recognition purposes. We first assume that an image I(x,y) is a black box consisting of a combination of illumination and reflectance. A new approximation is proposed to enhance the illumination removal phase. As illumination resides in the low-frequency part of image, it is reasonable to consider the use of a high-performance multiresolution transformation to first accurately separate the frequency components of an image. The double-density dual-tree complex wavelet transform (DD-DTCWT), possesses three core advantages, i.e., the transformation is (i) shift-invariant, (ii) directionally selective with no checkerboard effect, (iii) enriched by extra wavelets interpreted as double-density. The output of the first phase is sent to a DD-DTCWT unit to be decomposed into frequency subbands. High-frequency subbands are thresholded and an inverse DD-DTCWT is then applied to subbands to construct a low-frequency raw image, which is followed by a fine-tuning process. Finally, after extracting a mask, feature vector is formed and the principal component analysis (PCA) is used for dimensionality reduction which is then proceeded by the extreme learning machine (ELM) as a classifier to evaluate the performance of the proposed algorithm for face recognition under varying illumination. Unlike similar works, the proposed method is free of any prior information about the face shape, it is systematic and easy to implement, and it can be applied separately on each image. Furthermore, the proposed method which is significantly faster than similar techniques presents a robust behavior against the reduction in the number of images required for the training cycle. Several experiments are performed employing the available well-known databases such as the Yale B, Extended-Yale B, CMU-PIE, FERET, AT&T, and the Labeled Faces in the Wild (LFW). Illustrative examples are given and the results compare favorably to the current results in the literature.

New adaptive method for image denoising with keeping details efficiently

In order to keep more details while denoising an efficient local adaptive image denoising algorithm based on the Double-density Dual-tree Complex Wavelet Transform DD DT CWT was proposed.The principles and characteristics of the DD DT CWT were analyzed and a Bivariate Shrinkage Function BSF was derived.The noisy image was firstly decomposed by the DD DT CWT then according to the statistical properties of wavelet coefficients and the dependency of inter-scale with intra-scale coefficients the wavelet coefficients were shrunk by the BSF with local variance estimation and finally the denoised image was reconstructed by the shrunk coefficients.The experimental results prove that the proposed algorithm is efficient in noise removal and details reservation and can improve the visual quality of the denoised image.