Unsymmetrically Trimmed Mean Filter for Noise Removal of Robot Vision in Dark Environments

2018 ◽  
Author(s):  
Yao Huang ◽  
Changxin Zhou ◽  
Jianyu Yang ◽  
Zhanpeng Shao ◽  
Youfu Li
Keyword(s):  
Robot Vision ◽  
Noise Removal ◽  
Trimmed Mean ◽  
Mean Filter

scholarly journals Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2782
Author(s):  
Krystian Radlak ◽  
Lukasz Malinski ◽  
Bogdan Smolka
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Network Architecture ◽  
Noise Suppression ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Vision Systems ◽  
Mean Filter ◽  
Computer Vision Systems ◽  
Active Research

Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.

An Adaptive Filtering Algorithm Based on Improved Gray Relational Analysis

2011 ◽  
Vol 148-149 ◽  
pp. 483-486
Author(s):  
Chun Yan Huang ◽  
Yan Ling Li
Keyword(s):  
Adaptive Filtering ◽  
Noise Removal ◽  
Image Features ◽  
Gray Relational Analysis ◽  
Filtering Algorithm ◽  
Relational Analysis ◽  
Mean Filter ◽  
Gray Relation Analysis ◽  
Mixed Noise ◽  
Mixed Noise Removal

Because of the characteristic of the gray relation analysis and the advantage of the alpha-trimmed mean filter, an efficient technique for mixed noise removal in images was proposed. This algorithm can adjust the filter coefficients adaptively according to various pieces of the image features. Experiment results show that the proposed algorithm which greatly improved efficiently, it not only can remove mixed noise in image, but also can keep the details of the image.

scholarly journals Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Hongjin Ma ◽  
Yufeng Nie
Keyword(s):  
Diffusion Model ◽  
Anisotropic Diffusion ◽  
Impulse Noise ◽  
Noise Removal ◽  
Image Features ◽  
Weighted Mean ◽  
Optimal Direction ◽  
Mean Filter ◽  
Mixed Noise ◽  
Mixed Noise Removal

A mixed noise removal algorithm combining adaptive directional weighted mean filter and improved adaptive anisotropic diffusion model is proposed. Firstly, a noise classification method is introduced to divide all pixels into two types as the pixels corrupted by impulse noise and the pixels corrupted by Gaussian noise. Then an adaptive directional weighted mean filter is developed to remove impulse noise, which can adaptively select the optimal direction template from twelve direction templates and replace the gray level of each impulse noise corrupted pixel by the weighted mean gray level of pixels on the optimal direction template. Finally, an improved adaptive anisotropic diffusion model is developed to remove Gaussian noise in the initial denoised image, which can finely classify image features as smooth regions, edges, corners, and isolated noises by characteristic parameters and variance parameter and conduct adaptive diffusion for different image features by designing reasonable eigenvalues of diffusion tensor. A large number of experimental results show that the proposed algorithm outperforms many existing main mixed noise removal methods in terms of image denoising and detail preservation.

scholarly journals Application of fuzzy logic in finding the optimal filter in optoacoustics problems

2021 ◽  
pp. 43-54
Author(s):  
A. G. Rudnitskii ◽  
M. A. Rudnytska ◽  
L. V. Tkachenko ◽  
E. D. Pechuk
Keyword(s):  
Medical Practice ◽  
Early Stage ◽  
Median Filter ◽  
A Priori ◽  
Three Dimensional ◽  
Speckle Noise ◽  
Noise Removal ◽  
Arithmetic Mean ◽  
Optimal Filter ◽  
Mean Filter

Denoising is an important step in the early stage of signal preprocessing in optoacoustic applications. The efficiency of such modern noise removal methods as wavelet or curvlet filtering depends significantly on the numerical combinations and forms of wavelet transform parameters, and the multidimensional extension of such filters is rather non-trivial. These issues are serious obstacle for using of these highly effective filters in the tasks of optoacoustic reconstruction, especially in real laboratorial or medical practice. The objective of our study was to find the optimal filter, convenient for use in laboratorian and medical practice, when the types of noise are a priori unknown, and the filter settings should not take much time. In the offered work spatial filters which have only one parameter of adjustment - the size of a window are considered. Three-dimensional extensions of such well-established denoising techniques, as mean filter, median filter, their adaptive variants (Wiener spatial filter and modified median filter), as well as iterative truncated arithmetic mean filter were analyzed. The proposed filters were tested on a test set that contains versions of Shepp-Logan's three-dimensional phantom with mixtures of Gaussian and alpha-stable noise, as well as speckle noise. The identification of the best filter for simultaneous suppression of these types of interference was carried out using the theory of fuzzy sets. In our tests, a modified median filter and an iterative truncated arithmetic mean filter were rated as the best choice when the goal is to minimize aberrations when noise is not known a priory.

scholarly journals Periodic Noise Removal in Strain and Natural Images Using 2-D Fast Fourier Transform

2013 ◽  
pp. 90-94
Author(s):  
Mandeep Kaur ◽  
Dinesh Kumar ◽  
Ekta Walia ◽  
Manjit Sandhu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Domain ◽  
Noise Removal ◽  
Natural Images ◽  
Periodic Pattern ◽  
Periodic Noise ◽  
Mean Filter ◽  
The Mean

This paper presents a 2-D FFT removal algorithm for reducing the periodic noise in natural and strain images. For the periodic pattern of the artifacts, we apply the 2-D FFT on the strain and natural images to extract and remove the peaks which are corresponding to periodic noise in the frequency domain. Further the mean filter applied to get more effective results. The performance of the proposed method is tested on both natural and strain images. The results of proposed method is compared with the mean filter based periodic noise removal and found that the proposed method significantly improved for the noise removal.

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