Multifractal characterization of single wall carbon nanotube thin films surface upon exposure to optical parametric oscillator laser irradiation

Removing noise from images while keeping its important details unchanged is a challenging issue in image restoration. In this paper, we propose a novel approach based on partial differential equati...

Adaptive Restoration of Multispectral Datasets used for SVM classification

In many fields, images become a tool that contains data such as medical images. However, the image not only has blur or noise, but also has blur and noise pair. The aim of deblurring and denoising image is to remove blur and noise detail but this process helps keep edges features and its information. In this paper, we have proposed a method for increasing the quality of medical images based on the combination of filters in ridgelet domain. The proposed method uses ridgelet transform combined with Bayesian thresholding for denoising process and uses Wiener filter for deblurring process in ridgelet domain. For demonstrating the superiority of the proposed method, we have compared the results with the other recent methods available in literature.

Increasing the Quality of Medical Images Based on the Combination of Filters in Ridgelet Domain

The invention discloses a thresholding image denoising method based on nonsubsampled Contourlet transformation, and the problem that the effect of an existing denoising method is bad is mainly solved. The realization method comprises the steps of inputting a noisy image, conducting the nonsubsampled Contourlet transformation on the noisy image, conducting a sub-band noise variance estimation and an estimation of a standard error of a coefficient in a transform domain, setting a reasonable threshold, conducting threshold processing on an NSCT coefficient through an improved semi-soft threshold function without processing a low frequent component, and obtaining a de-noised image through conducting nonsubsampled Contourlet inverse transformation on a processed NSCT coefficient. According to the thresholding image denoising method based on the nonsubsampled Contourlet transformation, noise in a natural image with white Gaussian noise can be effectively removed, and image information edges are kept as much as possible at the same time, so that the best recovery of an original image is obtained, and the thresholding image denoising method based on the nonsubsampled Contourlet transformation has good application prospect and great development potential.

Thresholding image denoising method based on nonsubsampled Contourlet transformation

In clinical diagnostics, the diagnostic approach based on images obtained from equipment of which medical machine (diagnostic imaging) plays an important role. However, most of medical images have blur combined with noise. There are many reasons to create blur combined with noise in medical images such as the environment, capture device, technician's skills, etc. This problem will affect the process diagnose. In this paper, we proposed a new method to improve the quality of medical images. The proposed method uses cycle spinning combined with Kernels set in shearlet domain. Our algorithm removes the noise and blur details in shearlet domain, and must not the value of point-spread function (PSF). The proposed algorithm not only significantly improves the edge accuracy, but also reduces the loss of information in medical image.

Improving the quality of medical images in shearlet domain

Image Denoising Method with Adaptive Bayes Threshold in Nonsubsampled Contourlet Domain

Aiming at the deficiency of traditional method in evaluating micro surface topography, an evaluation method is advanced based on multi-fractal spectrum. In this method, the probability measure of the gray image of micro surface topography is counted in the improved box-counting method, and the multi-fractal spectrums are extracted by the Legendre transformation of scale exponent. Based on the multi-fractal spectrum, the spectrum width, height interval and asymmetry degree are extracted, and micro surface topography are evaluated according to the parameters. Evaluation experiments of surface topographies are carried out to prove the value of multi-fractal spectrums, and the result shows that multi-fractal spectrum can overcome the deficiency of traditional method, and surface topography can be evaluated accurately by its parameters.

Mingwei Chui

Papers

Image Denoising Method with Adaptive Bayes Threshold in Nonsubsampled Contourlet Domain

Evaluation for Micro Surface Topography Based on Multi-fractal Spectrum