Enhanced multi-baseline unscented Kalman filtering phase unwrapping algorithm

This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optimal path-following strategy based on phase quality estimate function. The enhanced joint phase gradient estimator can accurately and effectively extract the phase gradient information of wrapped pixels from noisy interferograms, which greatly increases the performances of the proposed method. The optimal path-following strategy ensures that the proposed algorithm simultaneously performs noise suppression and phase unwrapping along the pixels with high-reliance to the pixels with low-reliance. Accordingly, the proposed algorithm can be predicted to obtain better results, with respect to some other algorithms, as will be demonstrated by the results obtained from synthetic data.

An Enhanced Half Circle Family Window

in this paper,a new window,enhanced half circle family window,is discussed.

Refinement of Enhanced Speech Using Hybrid-Median Filter and Harmonic Regeneration Approach

This study proposes a post-processor to improve the harmonic structure of a vowel in an enhanced speech, enabling the speech quality to be improved. Initially, a speech enhancement algorithm is employed to reduce the background noise for a noisy speech. Hence the enhanced speech is post-processed by a hybrid-median filter to reduce the musical effect of residual noise. Since the harmonic spectra are impacted by background noise and a speech enhancement process, the quality of a vowel is deteriorated. A harmonic regenerated method is developed to improve the quality of post-processed speech. Experimental results show that the proposed method can improve the quality of post-processed speech by adequately regenerating harmonic spectra.

Multi-level denoising and enhancement method based on wavelet transform for mine monitoring

Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented. Firstly, we used directional median filter to effectively reduce impulse noise in the spatial domain, which is the main cause of noise in mine. Secondly, we used a Wiener filtration method to mainly reduce the Gaussian noise, and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain. This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits, and effectively reduce impulse noise and Gaussian noise in a coal mine, while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.

Metaheuristic Based Noise Identification and Image Denoising Using Adaptive Block Selection Based Filtering

Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to require these techniques. In real time, images do not contain a single noise, and instead they contain multiple types of noise distributions in several indistinct regions. This paper presents an image denoising method that uses Metaheuristics to perform noise identification. Adaptive block selection is used to identify and correct the noise contained in these blocks. Though the system uses a block selection scheme, modifications are performed on pixel- to-pixel basis and not on the entire blocks;hence the image accuracy is preserved. PSO is used to identify the noise distribution, and appropriate noise correction techniques are applied to denoise the images. Experiments were conducted using salt and pepper noise, Gaussian noise and a combination of both the noise in the same image. It was observed that the proposed method performed effectively on noise levels up-to 0.5 and was able to produce results with PSNR values ranging from 20 to 30 in most of the cases. Excellent reduction rates were observed on salt and pepper noise and moderate reduction rates were observed on Gaussian noise. Experimental results show that our proposed system has a wide range of applicability in any domain specific image denoising scenario, such as medical imaging, mammogram etc.

Enhanced quantitative X-ray phase-contrast images using Foucault differential filters

Several X-ray phase visualization methods are being real- ized for imaging of phase objects, such as biological and polymeric specimens. Grating-based phase-contrast imaging using a source-grating-attached X-ray tube that provides partially coherent X rays is one of the most successful methods in this field.

Improved multilevel filters to enhance infrared small target

We propose improved multilevel filters （IMLFs） involving the absolute value operation into the algorithmic framework of traditional multilevel filters （MLFs） to improve the robustness of infrared small target enhancement techniques under a complex infrared cluttered background. Compared with the widely used small target enhancement methods which only deal with bright targets, the proposed technique can enhance the infrared small target, whether it is bright or dark. Experimental results verify that the proposed technique is efficient and practical.