Context-based denoising of images using iterative wavelet thresholding

The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the work has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to the non-stationarity of images. We model each coefficient as a random variable with the generalized Gaussian prior with unknown parameters. Context modeling is used to estimate the parameters for each coefficient, which are then used to adapt the thresholding strategy. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than optimal uniform thresholding.

On the basis of wavelet theory, a novel Adaptive wavelet thresholding method (AWT) is proposed for the ECG signal enhancement. The best base wavelet for ECG signal filtering can be automatically obtained through the cross correlation coefficient and the energy to entropy ratio. The variable universal threshold (VarUniversal) is applied to different decomposition level so as to suppress diverse noise. To achieve a smooth cut-off transition, an identical correlation shrinkage function (IcoShrinkage) is also adopted in the AWT according to its correlation coefficients with the hard thresholding and the soft thresholding. The performance of AWT is compared with four threshold approaches and six shrinkage functions, respectively, on the basis of 150 practical ECG signals of 30 subjects. The filtering results reveal that the AWT can adaptively choose an optimal base wavelet for a specific ECG signal. With the VarUniversal threshold and IcoShrinkage, the AWT obtains the better filtering results than the other compared methods.

Considering the noise problem of the acquisition signals from mechanical transmission systems, a novel denoising method is proposed that combines Variational Mode Decomposition (VMD) with wavelet thresholding. The key innovat... | Find, read and cite all the research you need on Tech Science Press

Wavelet threshold denoising can easily cause edge blur, while total variational denoising is insufficient to achieve image smooth area denoising. Therefore, this paper proposes a new denoising method combining the total variational model and the adaptive wavelet threshold. Firstly, the original noisy image is processed by the total variational model, the improved Prewitt operator is used to extract the edge of the processed image, and the edge denoising image is obtained. Then, the original noisy image is divided into edge and non-edge images by the improved Prewitt operator, and the non-edge image is de-noised by the adaptive wavelet threshold method to obtain the non-edge denoising image. Finally, after denoising, the edge image and non-edge image are superimposed to achieve high-definition restoration of medical images. Experimental results show that the proposed method can effectively denoise medical images and preserve the original edges and details in the images.KeywordsImage denoisingTotal variational modelPrewitt operatorWavelet transform

EOG artifact removal using a wavelet neural network

This paper presents a robust adaptive moving human detection and recognition method in videos. The human detection method consists of modified moving average background model with supportive secondary model and an adaptive threshold selection model based on Gaussian distribution. The moving average background model is used for background modeling and the background subtraction system is used to provide foreground image through difference image between current image and background model. The adaptive threshold method is used to simultaneously update the system to environment changes. The modified human model consists of five parts with robust features to facilitate human recognition process. For recognition purpose Support Vector Machine has been used as classifier. Experimental results show the effectiveness of proposed system.

본 논문에서는 2차원 바코드 영상을 위한 적응적 이진화 방법을 제안한다. 적응형 이진화 방법은 조명의 영향을 최소화하여 이진화를 수행하는 기술이다. 적응적 이진화 방법은 주로 문서 영상에 맞게 발전되어 왔다. 기존 방법들은 적응적 이진화에서 사용되는 박스에 대한 크기 설정 문제를 가지고 있다. 이 문제로 기존 방법들은 이차원 바코드 영상 인식에 적용하기에 부적절하다. 문제점을 극복하기 위해 먼저 박스크기와 기존 방법들의 문제점을 분석하고, 이를 기반으로 적분영상을 사용한 새로운 적응형 이진화 방법을 소개한다. 제안한 방법의 성능 입증을 위해 기존의 방법과 속도, 성능 비교 실험을 수행하였고 실험 결과는 기존 방법보다 우수함을 입증하였다. In this paper, we propose an adaptive thresholding method to binarize two-dimensional barcode images. Adaptive thresholding methods that minimize light effects convert an original image into a binary image. The methods are applied to document image binarization. The methods, however, have problems of determining box size used in adaptive thresholding. thus, they inappropriate to use in recognition of two-dimensional barcode images. To overcome the problem, we analysis the problem and propose a new adaptive threshold method using the integral image. To show the effectiveness of our method, we compared our method with the well-known existing methods in terms of visual quality and processing time. The experimental result indicates that the proposed method is superior to the existing method.

Adaptive frame-based color image denoising

The world is marching towards digital communication and digital video surveillance is an indispensable part of it. Moving object detection and face detection play an important role in the video surveillance applications. It is a part of security oriented applications such as traffic systems, hospitals, banks. Background subtraction algorithm is used in order to detect the foreground. Due to the light intensity variations in dynamically changing environment, background subtraction module needs an adaptive threshold value for each input video frame. The pixel values above the threshold value are detected as foreground pixels. The range of adaptive threshold values usually varies from 10 to 85 in the proposed work. This gives a better detection capability in moving object detection wherein PSNR of 32.39 is obtained when two detected images with and without adaptive threshold are chosen as inputs to the PSNR block. Face detection is treated as an extension module of moving object detection wherein the input face image is subtracted from the test image with the help of adaptive threshold but finally the matching unit is the additional component which decides the match based on the global threshold value which is chosen as 183000 here. Here, instead of detecting the moving body, only face is considered for detection. Since real time videos need to be processed using FPGA kits, an area efficient moving object and face detection system is designed using Spartan 6 FPGA kit. The number of slice registers used has reduced from 365 to 320 in case of moving object detection, from 861 to 537 in case of face detection. The results obtained reveal that the use of adaptive threshold concept results in better detection process with PSNR of 32.39.

This paper reviews wavelet-based denoising techniques and presents an effective new algorithm using adaptive threshold method, processing of detailed and approximate coefficient, the optimal choice of level of decomposition for noise reduction in non-stationary signals. The method is based on computing of orthogonal wavelet transform and level-dependent estimation of the threshold. The proposed algorithm is tested with different wavelet bases: Haar, Daubechies; Cubic B-splines; Symplet; Coinflet; biorthogonal wavelets and a comparative analysis is performed. The proposed algorithm was applied to the real signals and has the task of assessing the application of different wavelet bases, threshold techniques, decomposition levels, and times to execute the procedures for accurate and fast-track procedures to reduce interference. The proposed algorithm can be used for single channel real non-stationary signals. For the purpose of comparative analysis of different methods a software program was created by the author, with graphical user interface, that implements the basic denoising techniques, enables the setting of the decomposition level, the wavelet basis, the size of the test signal, and calculates the evaluation characteristics of the denoising process. The software application enables the testing of new denoising algorithms, allows the addition of other types of noise and the investigation of their effects on the signals and is realized in the MATLAB development environment. The proposed results demonstrate that the presented algorithm is suitable for denoising of non-stationary real signals.

Speech enhancement using a wavelet thresholding method based on symmetric Kullback–Leibler divergence

Single sensor digital color cameras capture only one of the three primary colors at each pixel and a process called color demosaicking (CDM) is used to reconstruct the full color images. Most CDM algorithms assume the existence of high local spectral redundancy in estimating the missing color samples. However, for images with sharp color transitions and high color saturation, such an assumption may be invalid and visually unpleasant CDM errors will occur. In this paper, we exploit the image nonlocal redundancy to improve the local color reproduction result. First, multiple local direc- tional estimates of a missing color sample are computed and fused according to local gradients. Then, nonlocal pixels similar to the esti- mated pixel are searched to enhance the local estimate. An adaptive thresholding method rather than the commonly used nonlocal means filtering is proposed to improve the local estimate. This allows the final reconstruction to be performed at the structural level as op- posed to the pixel level. Experimental results demonstrate that the proposed local directional interpolation and nonlocal adaptive thresh- olding method outperforms many state-of-the-art CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images. © 2011

Wavelet thresholding with uniform threshold has shown some success in denoising. For images, we propose that this can be improved by adjusting thresholds spatially, based on the rationale that detailed regions such as edges and textures tolerate some noise but not blurring, whereas smooth regions tolerate blurring but not noise. The proposed algorithm is based on multiscale edge detection and image segmentation and then thresholding the coefficients of different regions with adaptive thresholds.

In the paper, a practical method for road detection is proposed. Many features of vehicle are introduced to detect the preceding vehicles. According to the characteristics of lane marks and vehicles, the improved Sobel operator convolution kernel is introduced in the image preprocessing to enhance the robustness of the algorithm. In order to be feasible to the change of illumination and road conditions, the adaptive double threshold method is introduced to get the binary image, the edges which are needed during detection of lane marks and front vehicles are extracted with Hough Transformation and SUSAN algorithm based on adaptive threshold in different contrast ratio. In order to apply the information to implement the lane departure warming system and forward collision warming system, both distances from host vehicle to left and right lane lines and preceding vehicles are estimated according to the inverse projection. Experiment results indicate that this algorithm is feasible for different road conditions and can realize the detection and tracking of the lane lines and vehicles with robustness and efficiency simultaneously.

<span>Segmentation of blood vessels (BVs) from retinal image is one of the important steps in developing a computer-assisted retinal diagnosis system and has been widely researched especially for implementing automatic BV segmentation methods. This paper proposes an improvement to an existing retinal BV (RBV) segmentation method by combining the trainable B-COSFIRE filter with adaptive thresholding methods. The proposed method can automatically configure its selectivity given a prototype pattern to be detected. Its segmentation performance is comparable to many published methods with the advantage of robustness against noise on retinal background. Instead of using grid search to find the optimal threshold value for a whole dataset, adaptive thresholding (AT) is used to determine the threshold for each retinal image. Two AT methods investigated in this study were ISODATA and Otsu’s method. The proposed method was validated using 40 images from two benchmark datasets for retinal BV segmentation validation, namely DRIVE and STARE. The validation results indicated that the segmentation performance of the proposed unsupervised method is comparable to the original B-COSFIRE method and other published methods, without requiring the availability of ground truth data for new dataset. The Sensitivity and Specificity values achieved for DRIVE and STARE are 0.7818, 0.9688, 0.7957 and 0.9648, respectively.</span>