A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. The method proposed in this paper is a fully automatic 3D blockwise version of the nonlocal (NL) means filter with wavelet subbands mixing. The proposed wavelet subbands mixing is based on a multiresolution approach for improving the quality of image denoising filter....

In this paper a hybrid denoising algorithm which combines spatial domain bilateral filter and hybrid thresholding function in the wavelet domain is proposed. The wavelet transform is used to decompose the noisy image into its different subbands namely LL, LH, HL, and HH. A two stage spatial bilateral filter is applied. The first stage is applied on the noisy image before wavelet decomposition. ...

We propose a soft thresholding approach to the minimum description length wavelet denoising. Our method is based on combining two-part coding with normalized maximum likelihood universal models to give a soft thresholding denoising criterion. Experiments with the proposed MDL soft thresholding method indicate that our denoising criterion leads to fairly similar performance as with the well-know...

Since the last decade the study of the wavelet transform has attracted many efforts and made great progresses. However, the research interest is still growing. In this thesis, we investigate the applications of the wavelet transform to image and video processing. We first study the wavelet transform mainly from a statistical point of view. Specifically, we study the statistics of the wavelet co...

Bowel sounds signal (BS) is one of the important human physiological signals, analysis of the BS signal can then study gastrointestinal physiology and implement direct and effective diagnosis of gastrointestinal disorders. Use different threshold denoising methods denoising the original bowel sounds, simulated in the environment of MATLAB, then compared the denoising results and analyzed the ad...

In this paper, intravascular ultrasound (IVUS) grayscale images, acquired with a single-element mechanically rotating transducer, are processed with wavelet denoising and region-based segmentation to extract various layers of lumen contours and plaques. First, IVUS volumetric data is expanded on complex exponential wavelet-like basis functions, also known as Brushlets, which are well localized ...

Methods based on thresholding of wavelet coefficients have been found to be popular in the estimation of biological signals from noisy environment. Hard and soft filters are most commonly used in these methods. In this paper a novel thresholding filter for wavelet shrinkage estimation of biological signals is proposed. The proposed novel filter is applied using Visu Shrink rule and top rule to ...

Image denoising is the manipulation of the image data to produce a visually high quality image. The existing or current denoising algorithms or approaches are filtering approach, multifractal approach and wavelet based approach. Different noise models include noise as additive and multiplicative type. They include Gaussian noise, salt and pepper noise (impulsive noise), Brownian noise and speck...

De-noising of SPECT and PET images is a challenging task due to the inherent low signal-to-noise ratio of acquired data. Wavelet based multiscale denoising methods typically apply thresholding operators on sub-band coefficients to eliminate noise components in spatial-frequency space prior to reconstruction. In the case of high noise levels, detailed scales of sub-band images are usually domina...

Discrete wavelet transformations have became indispensable analytical tools in data compression and data denoising. In this paper we give some empirical accounts of wavelet transformations and propose novel thresholding and wavelet selection methods. This is achieved via connections with measures of inequality, that have been used in economics for a long time. We compare our methods with standa...