http://www.math.hkbu.edu.hk/ICM/pdf/10-05.pdf

Restoration of images corrupted by mixed Gaussian-impulse noise via l1-l0 minimization

Restoration of images corrupted by Gaussian and uniform impulsive noise

An enhancement method for X-ray image via fuzzy noise removal and homomorphic filtering

Dictionary learning based impulse noise removal via L1-L1 minimization

Quaternion switching filter for impulse noise reduction in color image

Impulse noise suppression with an augmentation of ordered difference noise detector and an adaptive variational method

In this paper we propose a combined method for removing impulse noise. In the first phase, we use an efficient detector, called the Statistics of Ordered Difference Detector (SODD) to identify pixels which are likely to be corrupted by impulse noise. The proposed SODD can yield very high noise detection accuracy at high noise density. This noise detection phase is crucial for the following noise removal. In the second phase, only these noise candidates are restored using the variational method. Edges and noise free pixels of images filtered by our combined method are preserved. Simulation results indicate that the proposed method is significantly better than those using other impulse noise reduction filters.

A variational method with a noise detector for impulse noise removal

This paper describes a new aerial images segmentation algorithm. Kernel Matching Pursuit (KMP) method is introduced to deal with the nonlinear distribution of the man-made objects' features in the aerial images. In KMP algorithm, a lot of training samples containing substantive information are used to detect the man-made objects. With KMP classifier, pixels in large aerial images will be labeled as different prediction values, which can be classified linearly. Then the modified Mumford-Shah model, which comprises the features of the KMP prediction values, is built to segment the aerial image by necessary level set evolution. The proposed method is proven to be effective by the results of experiments.

A Kernel Matching Pursuit Approach to Man-Made Objects Detection in Aerial Images

We propose a two-stage method for removing impulse noise in images. In the first stage, we use a novel noise detector, called the augmentation-of-ordered-difference detector, to identify pixels that are likely to be corrupted by impulse noise. This detector achieves accurate impulse-noise detection by augmenting the difference between the center pixel's intensity value and its neighbors' intensity values in a local window. In the second stage, noise pixels are restored using an iterative and adaptive median-based filter. This filter is adaptive according to the number of noise pixels in the filtering window. Thus, noise-free pixels are unaltered, and noise pixels are filtered by the adaptive filter. Simulation results indicate that the proposed method is better than other impulse-noise reduction filters in suppressing impulse noise and preserving image details.

Shoushui Chen

Papers

Impulse noise suppression with an augmentation of ordered difference noise detector and an adaptive variational method

A variational method with a noise detector for impulse noise removal

A Kernel Matching Pursuit Approach to Man-Made Objects Detection in Aerial Images

Two-stage method for impulse-noise removal