Inverse Stationary Wavelet Transform Research Articles

Improved Procedure for Multi-Focus Image Quality Enhancement Using Image Fusion with Rules of Texture Energy Measures in the Hybrid Wavelet Domain

Feature extraction is a collection of the necessary detailed information from the given source, which holds the information for further analysis. The quality of the fused image depends on many parameters, particularly its directional selectivity and shift-invariance. On the other hand, the traditional wavelet-based transforms produce ringing distortions and artifacts due to poor directionality and shift invariance. The Dual-Tree Complex Wavelet Transforms (DTCWT) combined with Stationary Wavelet Transform (SWT) as a hybrid wavelet fusion algorithm overcomes the deficiencies of the traditional wavelet-based fusion algorithm and preserves the directional and shift invariance properties. The purpose of SWT is to decompose the given source image into approximate and detailed sub-bands. Further, approximate sub-bands of the given source are decomposed with DTCWT. In this extraction, low-frequency components are considered to implement Texture Energy Measures (TEM), and high-frequency components are considered to implement the absolute-maximum fusion rule. For the detailed sub-bands, the absolute-maximum fusion rule is implemented. The texture energy rules have significantly classified the image and improved the output image’s accuracy after fusion. Finally, inverse SWT is applied to generate an extended fused image. Experimental results are evaluated to show that the proposed approach outperforms approaches reported earlier. This paper proposes a fusion method based on SWT, DTCWT, and TEM to address the inherent defects of both the Parameter Adaptive-Dual Channel Pulse coupled neural network (PA-DCPCNN) and Multiscale Transform-Convolutional Sparse Representation (MST-CSR).

Intelligent fuzzy system for automatic artifact detection and removal from EEG signals

The EEG signals were used in many medical and technological applications such as diagnosis of diseases, rehabilitation of disabled peoples, preventive healthcare, BCI (brain computer interface) systems. EEG signal is prone to the physiological and non-physiological artifacts which severely affect them and lead to its misinterpretation. An automatic method and/or algorithm; for handling EEG artifacts; is proposed. The proposed method is based on three statistical parameters (entropy, kurtosis and skewness), fuzzy inference system (FIS) and stationary wavelet transform (SWT). Each incoming EEG epoch is described using these three statistical parameters. Based on the extracted statistical parameters, the designed FIS decides if an epoch is artifactual or not. Then SWT is used to decompose the EEG epoch into detail and approximation coefficients. To reduce the effect of artifact removal, we propose to use other fuzzy inference systems, which allow to select the contaminated wavelet coefficients. The universal thresholding method is then applied to the corrupted coefficients. Finally, the inverse SWT applies to the thresholded and non-corrupted coefficients to restore the cleaned EEG signal. The performance of the proposed method in terms of amount of artifact removal and signal distortion is evaluated in three scenarios: fully simulated, semi-simulated, and real artifactual EEG data. The comparison of our method with some existing state-of-the-art methods shows the superiority of our method over others in terms of performance and computational time.

Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions

Underwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. Generally image denoising techniques that remove the noise from the images can use linear and non-linear filters. In this paper, wavelet based denoising method is used to reduce the noise from the images. The image is decomposed using Stationary Wavelet Transform (SWT) into low and high frequency components. The various shrinkage functions such as Visushrink and Sureshrink are used for selecting the threshold to remove the undesirable signals in the low frequency component. The high frequency components such as edges and corners are retained. Then the inverse SWT is used for reconstruction of denoised image by combining the modified low frequency components with the high frequency components. The performance measure Peak Signal to Noise Ratio (PSNR) is obtained for various wavelets such as Haar, Daubechies,Coiflet and by changing the thresholding methods.

Multi-focus image fusion for different datasets with super-resolution using gradient-based new fusion rule

Multi-focus image fusion methods combine two or more images which have blurred and defocused parts to create an all-in-focused image. All-in-focused image has more information, clearer parts and clearer edges than the source images. In this paper, a new approach for multi-focus image fusion is proposed. Firstly, the information of source images is enhanced using bicubic interpolation-based super-resolution method. Secondly, source images with high resolution are decomposed into four sub-bands which are LL (low-low), LH (low-high), HL (high-low) and HH (high-high) using Stationary Wavelet Transform with dmey (Discrete Meyer) filter. Then, a new fusion rule which depend on gradient-based method with sobel operator is implemented to create fused images with good visuality. The weight coefficients which show the importance rates of corresponding pixels in source images for fused image are calculated using designed formula based on gradient magnitudes. The each pixel of fused sub-bands is created using these weight coefficients and fused image is reconstructed using Inverse Stationary Wavelet Transform. Lastly, the performance evaluation of proposed method is measured using three different metrics which are objective, subjective and time criterion metrics. Besides these features, the new dataset which is different from the datasets in the literature is created and used firstly in this paper. The results show that the proposed method produces high quality images with clear edges and transmits most of the information of source images into all-in-focused image.

Two stage residual CNN for texture denoising and structure enhancement on low dose CT image

Background and objectiveX-ray computed tomography (CT) plays an important role in modern medical science. Human health problems caused by CT radiation have attracted the attention of the academic community widely. Reducing radiation dose results in a deterioration in image quality and further affects doctor’s diagnosis. Therefore, this paper introduces a new denoise method for low dose CT (LDCT) images, called two stage residual convolutional neural network (TS-RCNN). MethodsThere are two important parts with respect to our network. 1) The first stage RCNN is proposed for texture denoising via the stationary wavelet transform (SWT) and the perceptual loss. Specifically, SWT is performed on each normal dose CT (NDCT) image and generated four wavelet images are considered as the labels. 2) The second stage RCNN is established for structure enhancement via the average NDCT model on the basis of the first network’s result. Finally, the denoised CT image is obtained via inverse SWT. ResultsOur proposed TS-RCNN is trained on three groups of simulated LDCT images in 1123 images per group and evaluated on 129 simulated LDCT images for each group. Besides, to demonstrate the clinical application of TS-RCNN, we also test our method on the 2016 Low Dose CT Grand Challenge dataset. Quantitative results show that TS-RCNN almost achieves the best results in terms of MSE, SSIM and PSNR compared to other methods. ConclusionsThe experimental results and comparisons demonstrate that TS-RCNN not only preserves more texture information, but also enhances structural information on LDCT images.

Supervised Single Channel Speech Enhancement Based on Stationary Wavelet Transforms and Non-negative Matrix Factorization with Concatenated Framing Process and Subband Smooth Ratio Mask

In this paper, we propose a novel single channel speech enhancement approach that takes up the Stationary Wavelet Transform (SWT) and Nonnegative Matrix Factorization (NMF) with Concatenated Framing Process (CFP) and proposes Subband Smooth Ratio Mask (ssRM). Due to downsampling process after filtering, Discrete Wavelet Packet Transform (DWPT) suffers the absence of shift-invariance, and for this reason, some errors occur in the signal reconstruction and to mitigate the problem, firstly we use SWT and NMF with KL cost function. Secondly, we exploit the CFP to build each column of the matrix instead of using NMF directly to take advantage of smooth decomposition. Thirdly, we apply the Auto-Regressive Moving Average (ARMA) filtering process to the newly formed matrices for making the speech more stable and standardized. Finally, we propose an ssRM by combing the Standard Ratio Mask (sRM) and Square Root Ratio Mask (srRM) with Normalized Cross-Correlation Coefficients (NCCC) to take the advantages of them (sRM, srRM and NCCC). In short, the SWT divides the time-domain mixing speech signal into a set of subband signals and then framing and taking the absolute value of each subband signal, and we obtain nonnegative matrices. Then, we form the new matrices by applying the CFP where each column of the formed matrix contains five consequent frames of the nonnegative matrix and performs an ARMA filtering operation. After that, we apply NMF to each newly formed matrix and detect the speech components via proposed ssRM. Finally, the estimated signal can be achieved through them by applying inverse SWT. Our approach is evaluated using IEEE corpus and different types of noises. Objective speech quality and intelligibility improve significantly by applying this approach and outperforms related methods such as conventional STFT-NMF and DWPT-NMF.

A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion

Multi-focus image fusion combines two or more images which have different focus values of the same scene using fusion rules. The meaningful image is named all-in-focus image which is more informative and useful for visual perception. In this paper, a novel approach for multi-focus image fusion is proposed. The method is a hybrid method with super-resolution. Firstly, super-resolution method is applied to all source images to enhance information like contrast. Thus, low-resolution source images are converted to high-resolution source images. Secondly, due to decomposing these source images, Stationary Wavelet Transform (SWT) is implemented and images are divided into four sub-bands. These sub-bands are LL (low–low), LH (low–high), HL (high–low) and HH (high–high). LL is the approximation coefficient of source images and others are the detail coefficients of source images. For all these sub-bands, Principal Component Analysis (PCA) is implemented and maximum eigenvector of each sub-band of source images is selected separately to fuse images. Then, Inverse Stationary Wavelet Transform (ISWT) is used to reconstruct the fused sub-bands. Finally, to measure quality of the proposed method objectively, fused image is resized to original source image's size using interpolation based resizing method. To measure the success of method, different metrics without reference image and with reference image, are selected. Results show that the proposed method produce clear edges, good visual perception, good clarity and very few distortion. The proposed hybrid method is applied to produce better quality fused images. Results prove success of the approach in this area. Also visual and quantitative results are very impressive.

Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform

The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K<sub>c</sub>). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.

Image enhancement using thermal-visible fusion for human detection

An increased interest in detecting human beings in video surveillance system has emerged in recent years. Multisensory image fusion deserves more research attention due to the capability to improve the visual interpretability of an image. This study proposed fusion techniques for human detection based on multiscale transform using grayscale visual light and infrared images. The samples for this study were taken from online dataset. Both images captured by the two sensors were decomposed into high and low frequency coefficients using Stationary Wavelet Transform (SWT). Hence, the appropriate fusion rule was used to merge the coefficients and finally, the final fused image was obtained by using inverse SWT. From the qualitative and quantitative results, the proposed method is more superior than the two other methods in terms of enhancement of the target region and preservation of details information of the image.

A Novel Multi-Focus Image Fusion Method Based on Stationary Wavelet Transform and Local Features of Fuzzy Sets

The key issue of multi-sensor image fusion is how to accurately extract and fuse the high-quality pixels or coefficients of source images. Nevertheless, the so-called high-quality is an uncertain or fuzzy definition, which is very suitable for fuzzy theory to address this problem. By the integration of stationary wavelet transform (SWT) and fuzzy sets, this paper proposes a new multi-focus image fusion scheme, which can merge the important features of different source images into a fused image. First, the source images are decomposed by SWT to get a set of sub-images with different detailed features. Second, the Gaussian membership function (GMF) is utilized to get the fuzzy sets of sub-images data. Third, the local spatial frequency (LSF) is employed to extract the local features of the sub-images by the generated fuzzy sets. At last, the fusion rule is designed based on consistency verification to fuse the sub-images according to the LSF of fuzzy sets, and then inverse SWT (ISWT) is implemented to reconstruct the fused image. The experimental and contrastive results of different image sets show that the proposed method is an effective multi-focus image fusion scheme which can achieve better fusion effect than other methods.

Super-Resolution Image Reconstruction Using Dual-Mode Complex Diffusion-Based Shock Filter and Singular Value Decomposition

Super-resolution (SR) algorithms are widely used to overcome the hardware limitations of the low-cost image acquisition devices. In this paper, we present a single image SR (SISR) approach in wavelet domain, which simultaneously preserves the contrast and edge information. Our algorithm uses the notion of geometric duality to generate the initial estimation of unknown high-resolution (HR) image, by applying covariance-based interpolation. State-of-the-art wavelet techniques for SISR provide resolution enhancement by replacing the low-frequency subband with the input low-resolution image. This leads to non-uniform illumination in the super-resolved image. The proposed method exploits singular value decomposition to correct the low-frequency subband, as obtained via stationary wavelet transform (SWT). The modified low-frequency subband and the high-frequency subband images are subjected to Lanczos interpolation. The interpolated subbands are filtered by employing diffusion-based shock filter, which operates in the dual dominant modes. All the filtered subband images are fused to generate the final HR image, by applying inverse SWT. Our experimental analysis has demonstrated the superiority of the proposed method in preserving the edges with uniform illumination.

Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions

Air quality forecasting is an effective way to protect public health by providing an early warning against harmful air pollutants. In this paper, a model W-BPNN using wavelet technique and back propagation neural network (BPNN) is developed and tested to forecast daily air pollutants (PM10, SO2, and NO2) concentrations. Firstly, stationary wavelet transform (SWT) is applied to decompose historical time series of daily air pollutants concentrations into different scales, of which the information represents wavelet coefficients of air pollutant concentration. Secondly, the wavelet coefficients are used to train a BPNN model at each scale. The input data for forecasting contain the wavelet coefficients of the air pollutants concentrations 1-day in advance, and local meteorological data. The suitable groups of the input variables are determined by correlation analysis method. At last, the estimated coefficients of the BPNN outputs for all of the scales are employed to reconstruct the forecasting result through the inverse SWT. The proposed approach is tested using data during 1/1/2011 to 26/12/2011 in Nan'an District of Chongqing, China. The results show that the W-BPNN model has better forecasting performance for the three air pollutants than mono-BPNN model in terms of the statistics indexes (mean absolute percentage error, root mean square error and correlation coefficient criteria) and the forecasting accuracy of the number of relevant days of individual air quality index.

A stationary wavelet transform based approach to registration of planning CT and setup cone beam-CT images in radiotherapy.

Image registration between planning CT images and cone beam-CT (CBCT) images is one of the key technologies of image guided radiotherapy (IGRT). Current image registration methods fall roughly into two categories: geometric features-based and image grayscale-based. Mutual information (MI) based registration, which belongs to the latter category, has been widely applied to multi-modal and mono-modal image registration. However, the standard mutual information method only focuses on the image intensity information and overlooks spatial information, leading to the instability of intensity interpolation. Due to its use of positional information, wavelet transform has been applied to image registration recently. In this study, we proposed an approach to setup CT and cone beam-CT (CBCT) image registration in radiotherapy based on the combination of mutual information (MI) and stationary wavelet transform (SWT). Firstly, SWT was applied to generate gradient images and low frequency components produced in various levels of image decomposition were eliminated. Then inverse SWT was performed on the remaining frequency components. Lastly, the rigid registration of gradient images and original images was implemented using a weighting function with the normalized mutual information (NMI) being the similarity measure, which compensates for the lack of spatial information in mutual information based image registration. Our experiment results showed that the proposed method was highly accurate and robust, and indicated a significant clinical potential in improving the accuracy of target localization in image guided radiotherapy (IGRT).

An Adaptive Rayleigh-Laplacian Based MAP Estimation Technique for Despeckling SAR Images using Stationary Wavelet Transform

Removal of speckle noise from Synthetic Aperture Radar (SAR) images is an important step before performing any image processing operations on these images. This paper presents a novel Stationary Wavelet Transform (SWT) based technique for the purpose of removing the speckle noise from the SAR returns. Maximum a posteriori probability (MAP) condition which uses a prior knowledge is used to estimate the noise free wavelet coefficients. The proposed MAP estimator is designed for this purpose which uses Rayleigh distribution for modeling the speckle noise and Laplacian distribution for modeling the statistics of the noise free wavelet coefficients. The parameters required for MAP estimator is determined by technique used for parameter estimation after SWT. Moreover an Laplacian – Gaussian based MAP estimator is also applied and the parameter estimation is done using the same method used for the proposed algorithm. For the purpose of enhancing the visual quality and to restore more edge information, a wavelet based resolution enhancement technique is also used after applying the Inverse stationary Wavelet Transform (ISWT), using interpolation technique. The experimental results show that the proposed despeckling algorithm efficiently removes speckle noise from the SAR images and restores the edge information as well.

Research of fetal ECG extraction using wavelet analysis and adaptive filtering

Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.

A wavelet domain detail compensation filtering technique for InSAR interferograms

The quality of interferogram filtering affects the accuracy of interferometric synthetic aperture radar (InSAR) applications. This article presents a new wavelet domain filtering method for phase noise reduction in an InSAR interferogram. The method first transforms the real and imagery parts of the original interferogram into the wavelet domain using the stationary wavelet transform (SWT). Then the coefficients for each sub-band are filtered with detail compensation. Finally, the wavelet coefficients are reconstructed in the space domain by the inverse SWT. The results show that the proposed method can suppress the speckle effectively, maintain details of the interferogram well, and greatly reduce the number of residues.