In order to removes the ring effect in traditional image denoising algorithms using wavelet thresholding, the paper analyzes the wavelet coefficients of noise images, uses second-order central moment of HH1 sub-bands as the noise variance and computes threshold values; and then performs wavelet thresholding denoising on each image block. At last, the paper weights these denoised wavelet coeffic...

This paper investigates the problem of image reconstruction of compressed sensing. First, an improved smoothed l0 norm (ISL0) algorithm is proposed by using modified Newton method to improve the convergence speed and accuracy of classical smoothed l0 norm (SL0) algorithm, and to increase calculation speed and efficiency. The choice of algorithm parameter is discussed and the algorithm convergen...

Introduction: The main idea of Compressed Sensing is to exploit the fact that there is some structure and redundancy in most signals of interest. Clearly, the more we known about the signal and the more the information we encode into the signal processing algorithm, the better performance we can achieve. In this paper, we propose an adaptive compressed MRI sensing scheme that combined wavelet e...

Noise reduction in the wavelet domain can be expressed as an estimation problem in a Bayesian framework. So, the proposed distribution for the noise-free wavelet coefficients plays a key role in the performance of wavelet-based image/ video denoising. This paper presents a new image/video denoising algorithm based on the modeling of wavelet coefficients in each subband with a mixture of Laplace...

In this paper a wavelet shrinkage algorithm based on fuzzy logic is proposed to improve the tea leaf image. The Tea Leaf images are normally changes to unclear images by the presence of noise, low or high dissimilarity both in the edge area and also in the image area. The Fuzzy shrink is used to enhance the image. In exacting, intra-scale dependency within wavelet coefficients is modeled using ...

One of the advantages of the Discrete Wavelet Transform (DWT) compared to Fourier Transform (e.g. Discrete Cosine Transform DCT) is its ability to provide both spatial and frequency localization of image energy. However, WT coefficients, like DCT coefficients, are defined by magnitude as well as sign. While algorithms exist for the coding of wavelet coefficients magnitude, there are no efficien...

A novel digital watermarking for image authentication is proposed in this paper. Most previous proposed watermarking algorithms embed sequences of random numbers as watermarks. Here images are taken as watermarks for embedding. In the proposed approach, the host image is decomposed into wavelet coefficients. Local entropies of wavelet coefficients in the low-frequency subband are calculated by ...

recent studies on wavelet transform and fractal modeling applied on mammograms for the detection of cancerous tissues indicate that microcalcifications and masses can be utilized for the study of the morphology and diagnosis of cancerous cases. it is shown that the use of fractal modeling, as applied to a given image, can clearly discern cancerous zones from noncancerous areas. in this paper, f...

In recent years, wavelet shrinkage has become a very appealing method for data denoising and density function estimation. In particular, Bayesian modelling via hierarchical priors has introduced novel approaches for Wavelet analysis that had become very popular, and are very competitive with standard hard or soft thresholding rules. In this sense, this paper proposes a hierarchical prior that i...

Objective of this paper is to identify a person taking fingerprint as a biometric parameter using wavelet packet transform. Here both conventional discrete wavelet transform (DWT) and discrete wavelet packet transform (WPT) are used considering special basis function/matrix to extract the coefficients of basis functions those convey the most of the energy of the signal or image. Here top 5% coe...