scholarly journals Universal Impulse Noise Suppression Using Extended Efficient Nonparametric Switching Median Filter

2018 ◽  
Vol 214 ◽  
pp. 01003
Author(s):  
M. H. Suid ◽  
M. A. Ahmad ◽  
M. I. F. M. Hanif ◽  
M. Z. Tumari ◽  
M. S. Saealal
Keyword(s):  
Impulse Noise ◽  
Noise Suppression ◽  
Impulsive Noise ◽  
Median Filter ◽  
Noise Model ◽  
Restoration Technique ◽  
Switching Median Filter ◽  
Noise Models ◽  
Impulse Detector ◽  
Better Than

This paper presents a filtering algorithm called extended efficient nonparametric switching median (EENPSM) filter. The proposed filter is composed of a nonparametric easy to implement impulse noise detector and a recursive pixel restoration technique. Initially, the impulse detector classifies any possible impulsive noise pixels. Subsequently, the filtering phase replaces the detected noise pixels. In addition, the filtering phase employs fuzzy reasoning to deal with uncertainties present in local information. Contrary to the existing conventional filters that only focus on a particular impulse noise model, the EENPSM filter is capable of filtering all kinds of impulse noise (i.e. the random-valued and/or fixed-valued impulse noise models). Extensive qualitative and quantitative evaluations have shown that the EENPSM method performs better than some of the existing methods by giving better filtering performance.

FPGA-Based Re-Configurable Architecture for Window-Based Image Processing

2018 ◽  
pp. 273-310
Author(s):  
Kamarujjaman Sk ◽  
Manali Mukherjee ◽  
Mausumi Maitra
Keyword(s):  
Adaptive Filtering ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Digital Images ◽  
Median Filter ◽  
Hardware Architecture ◽  
Efficient Design ◽  
Filtering Method ◽  
Switching Median Filter ◽  
Noise Density

In this proposed book chapter, a simple but efficient presentation of Median Filter, Switching Median Filter, Adaptive Median Filter and Decision-Based Adaptive Filtering Method and their hardware architecture for FPGA is described for removal of up to 99% impulse noise from Digital Images. For hardware architecture, simulation is done using Xilinx ISE 14.5 software of XILINX. For implementation, these approaches utilize Genesys VIRTEX V FPGA device of XC5VLX50T device family. In this approach, we proposed an efficient design for suppression of impulse noise from digital images corrupted by up to 99% impulse noise using decision based adaptive filtering method as well as preserve the details of image. The method works in two different stages – noise detection using switching technique and finally noise suppression and restoration. Experimental results show that our method perform better in terms of PSNR below 80% noise density but above 80% noise density it is almost comparable with the latest methods.

FPGA-Based Re-Configurable Architecture for Window-Based Image Processing

2017 ◽  
pp. 1-46
Author(s):  
Kamarujjaman Sk ◽  
Manali Mukherjee ◽  
Mausumi Maitra
Keyword(s):  
Adaptive Filtering ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Digital Images ◽  
Median Filter ◽  
Hardware Architecture ◽  
Efficient Design ◽  
Filtering Method ◽  
Switching Median Filter ◽  
Noise Density

In this proposed book chapter, a simple but efficient presentation of Median Filter, Switching Median Filter, Adaptive Median Filter and Decision-Based Adaptive Filtering Method and their hardware architecture for FPGA is described for removal of up to 99% impulse noise from Digital Images. For hardware architecture, simulation is done using Xilinx ISE 14.5 software of XILINX. For implementation, these approaches utilize Genesys VIRTEX V FPGA device of XC5VLX50T device family. In this approach, we proposed an efficient design for suppression of impulse noise from digital images corrupted by up to 99% impulse noise using decision based adaptive filtering method as well as preserve the details of image. The method works in two different stages – noise detection using switching technique and finally noise suppression and restoration. Experimental results show that our method perform better in terms of PSNR below 80% noise density but above 80% noise density it is almost comparable with the latest methods.

scholarly journals Switching median filter for suppressing multi-pixel impulse noise

2021 ◽  
Vol 45 (4) ◽  
pp. 580-588
Author(s):  
A.A. Trubitsyn ◽  
E.Yu. Grachev
Keyword(s):  
Impulse Noise ◽  
Noise Suppression ◽  
Median Filter ◽  
Background Intensity ◽  
X Ray ◽  
Switching Median Filter ◽  
Image Blurring ◽  
Intensity Increment ◽  
Single Pixel ◽  
Image Details

This paper proposes a new switching median filter for suppressing multi-pixel impulse noise in X-ray images. A multi-pixel impulse is understood as a set of several neighboring pixels, the intensity of each significantly exceeds background intensity. Multi-pixel noise can occur, for example, due to the blooming effect, the reason being the limited value of pixel saturation capacity. This article defines the thresholds for the intensity increment relative to the eight immediate neighbors, above which the current pixel is processed by the median filter. The dependence of these thresholds on the number of pixels in an impulse is presented. The proposed algorithm is based on the median filtering process, which consists of several iterations. In this case, the filter has the smallest possible size, which minimizes image distortion during processing. In particular, to exclude a single-pixel impulse, pixel processing is turned on when intensity surge exceeds 3.5 with the grayscale value ranging from 0 to 1. At the same time, to exclude nine-pixel impulses, three iterations are required with the following thresholds: the first iteration with a threshold 2.0; the second iteration also with a threshold 2.0 and the third iteration with a threshold 3.5. The algorithm proposed was tested on real X-ray images corrupted by multi-pixel impulse noise. The algorithm is not only simple, but also reliable and suitable for real-time implementation and application. The efficiency of the technique is shown in comparison with other known filtering methods with respect to the degree of noise suppression. The main result of the testing is that only the proposed method allows excluding multi-pixel noise. Other advantage of the algorithm is its weak effect on the level of Gaussian noise leading to the absence of image blurring (or preserving image details) during processing.

scholarly journals LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 629 ◽  
Author(s):  
Shiguang Zhang ◽  
Ting Zhou ◽  
Lin Sun ◽  
Wei Wang ◽  
Baofang Chang
Keyword(s):  
Wind Speed ◽  
Regression Models ◽  
Historical Data ◽  
Least Square ◽  
Noise Model ◽  
Noise Distribution ◽  
Short Term ◽  
Mixed Noise ◽  
Noise Models ◽  
Better Than

Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square S V R of the Gaussian–Laplacian mixed homoscedastic ( G L M − L S S V R ) and heteroscedastic noise ( G L M H − L S S V R ) for complicated or unknown noise distributions. The ALM technique is used to solve model G L M − L S S V R . G L M − L S S V R is used to predict short-term wind speed with historical data. The prediction results indicate that the presented model is superior to the single-noise model, and has fine performance.

scholarly journals Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

2017 ◽  
Vol 2017 ◽  
pp. 1-18
Author(s):  
Hongyao Deng ◽  
Qingxin Zhu ◽  
Xiuli Song
Keyword(s):  
Total Variation ◽  
Color Image ◽  
Impulsive Noise ◽  
Median Filter ◽  
Noise Removal ◽  
Color Images ◽  
Natural Image ◽  
Noise Characteristics ◽  
Switching Median Filter ◽  
Vector Median

Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation L1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i) each channel in a color image is contaminated independently, and contaminative components are independent and identically distributed; (ii) in a natural image the gradients of different components of a pixel are similar to one another. This method divides components into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the components are divided into the corrupted and the noise-free components; if the image is corrupted by random-valued impulses, the components are divided into the corrupted, noise-free, and the possibly corrupted components. Components falling into different categories are processed differently. If a component is corrupted, modified total variation diffusion is applied; if it is possibly corrupted, scaled total variation diffusion is applied; otherwise, the component is left unchanged. Simulation results demonstrate its effectiveness.

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