Improved Wavelet Threshold Function Research Articles

Image Denoising Based on Improved Wavelet Threshold Function for Wireless Camera Networks and Transmissions

A new wavelet threshold denoising function and an improved threshold are proposed. It not only retains the advantages of hard and soft denoising functions but also overcomes the disadvantages of the continuity problem of hard denoising function and the constant deviation of the soft denoising function in the new method. In the case of the improved threshold conditions, the new threshold function has a better performance in outstanding image details. It can adapt to different images by joining an adjusting factor. Simulation results show that the new threshold function has a better ability of performing image details and a higher peak signal to noise ratio (PSNR).

Wavelet and fast bilateral filter based de-speckling method for medical ultrasound images

Speckle noise is an undesirable part of the ultrasound imaging process, since it can degrade the quality of ultrasound images and restrict the development of automatic diagnostic techniques for ultrasound images. Aiming at the problem of speckle noise, an improved de-speckling method for medical ultrasound images is proposed, which is based on the wavelet transformation and fast bilateral filter. According to the statistical properties of medical ultrasound image in the wavelet domain, an improved wavelet threshold function based on the universal wavelet threshold function is considered. The wavelet coefficients of noise-free signal and speckle noise are modeled as generalized Laplace distribution and Gaussian distribution, respectively. The Bayesian maximum a posteriori estimation is applied to obtain a new wavelet shrinkage algorithm. High-pass component speckle noise in the wavelet domain of ultrasound images is suppressed by the new shrinkage algorithm. Additionally, the coefficients of the low frequency signal in the wavelet domain are filtered by the fast bilateral filter, since the low-pass component of ultrasound images also contains some speckle noise. Compared with other de-speckling methods, experiments show that the proposed method has improved de-speckling performance for medical ultrasound images. It not only has better reduction performance than other methods but also can preserve image details such as the edge of lesions.

Improvement in Moving Target Detection Based on Hough Transform and Wavelet

ABSTRACTThe most fundamental problem in radar signal is the detection of a moving object or a physical phenomenon. This problem became more challenging to the signal processing and radar communities under severe interference. In order to effectively detect moving targets in strong noise conditions, we proposed a new method based on wavelet and Hough transform for multiple moving targets detection. First, the noise is removed from the received signal by the wavelet technique. We propose an improved wavelet threshold function. The new threshold function not only reduces constant error of soft thresholding but also improves the discontinuity of hard thresholding. Second, the image processing technique of the Hough transform is used. The benefits of this method include improved performance of detection and implementation flexibility, which indicates the effectiveness and robustness of the algorithm.

Application of an Improved Algorithm for Denoising of Wavelet Threshold in Palmprint Recognition

In the acquisition process of palmprint image, the image is often subject to interference from the outside noise.This noise will affect the future of Palmprint feature extraction. Denoising of wavelet is a common method for denoising of palmprint image. The defect of traditional soft and hard threshold functions in image denoising is analyzed. An improved threshold function is proposed on the basis of soft and hard threshold functions. The conventional hard, soft threshold functions and the improved function are used respectively in image denosing. Experiments prove that the improved wavelet threshold function is better than conventional soft, hard threshold function in denoising effect.

Improved wavelet denoising with dual-threshold and dual-factor function

Since the traditional wavelet threshold functions have some drawbacks such as the non-continuity on the points of threshold, large deviation of estimated wavelet coefficients, Gibbs phenomenon and distortion are generated and Signal-toNoise Ratio( SNR) can be hardly improved for the denoised signal. To overcome these drawbacks, an improved wavelet threshold function was proposed. Compared with the soft, hard, semi-soft threshold function and others, this function was not only continuous on the points of threshold and more convenient to be processed, but also was compatible with the performances of traditional functions and the practical flexibility was greatly improved via adjusting dual threshold parameters and dual variable factors. To verify this improved function, a series of simulation experiments were performed, the SNR and Root-MeanSquare Error( RMSE) values were compared between different denoising methods. The experimental results demonstrate that the smoothness and distortion are greatly enhanced. Compared with soft function, its SNR increases by 22. 2% and its RMSE decreases by 42. 6%.

The Ultrasonic Signal De-Noising Method Based on an Improved Wavelet Threshold Function

Making use of the advantages of the soft and hard threshold methods proposed by Donoho, this paper presents a deviation variable based on quadratic polynomial and differentiable improvement function, according to the characteristics of the underground water pipe leakage signal. In the paper we constructed the mathematical model of the function, utilized wavelet de-noising algorithm for the collected ultrasonic signal de-noising, compared the de-noising effect by means of the value of Signal to Noise Ratio (SNR) and Mean Squared Error (MSE). Results show that the new function has the better de-noising effect compared with the original threshold function.