An Effective Noise Adaptive Median Filter for Removing High Density Impulse Noises in Color Images

2016 ◽  
Vol 6 (2) ◽  
pp. 611 ◽  
Author(s):  
S. Abdul Saleem ◽  
T. Abdul Razak
Keyword(s):  
Median Filter ◽  
Color Images ◽  
High Density ◽  
Correct Selection ◽  
Median Filtering ◽  
Filtering Algorithms ◽  
Image Metrics ◽  
Quantitative Image ◽  
Degraded Images ◽  
Image Details

Images are normally degraded by some form of impulse noises during the acquisition, transmission and storage in the physical media. Most of the real time applications usually require bright and clear images, hence distorted or degraded images need to be processed to enhance easy identification of image details and further works on the image. In this paper we have analyzed and tested the number of existing median filtering algorithms and their limitations. As a result we have proposed a new effective noise adaptive median filtering algorithm, which removes the impulse noises in the color images while preserving the image details and enhancing the image quality. The proposed method is a spatial domain approach and uses the 3×3 overlapping window to filter the signal based on the correct selection of neighborhood values to obtain the effective median per window. The performance of the proposed effective median filter has been evaluated using MATLAB, simulations on a both gray scale and color images that have been subjected to high density of corruption up to 90% with impulse noises. The results expose the effectiveness of our proposed algorithm when compared with the quantitative image metrics such as PSNR, MSE, RMSE, IEF, Time and SSIM of existing standard and adaptive median filtering algorithms.

scholarly journals An Effective Noise Adaptive Median Filter for Removing High Density Impulse Noises in Color Images

2016 ◽  
Vol 6 (2) ◽  
pp. 611
Author(s):  
S. Abdul Saleem ◽  
T. Abdul Razak
Keyword(s):  
Median Filter ◽  
Color Images ◽  
High Density ◽  
Correct Selection ◽  
Median Filtering ◽  
Filtering Algorithms ◽  
Image Metrics ◽  
Quantitative Image ◽  
Degraded Images ◽  
Image Details

Images are normally degraded by some form of impulse noises during the acquisition, transmission and storage in the physical media. Most of the real time applications usually require bright and clear images, hence distorted or degraded images need to be processed to enhance easy identification of image details and further works on the image. In this paper we have analyzed and tested the number of existing median filtering algorithms and their limitations. As a result we have proposed a new effective noise adaptive median filtering algorithm, which removes the impulse noises in the color images while preserving the image details and enhancing the image quality. The proposed method is a spatial domain approach and uses the 3×3 overlapping window to filter the signal based on the correct selection of neighborhood values to obtain the effective median per window. The performance of the proposed effective median filter has been evaluated using MATLAB, simulations on a both gray scale and color images that have been subjected to high density of corruption up to 90% with impulse noises. The results expose the effectiveness of our proposed algorithm when compared with the quantitative image metrics such as PSNR, MSE, RMSE, IEF, Time and SSIM of existing standard and adaptive median filtering algorithms.

scholarly journals Reconstruction of Corrupted Image From Salt And Pepper Noise From Median Filter

2012 ◽  
pp. 4-6
Author(s):  
Vishal Gautam ◽  
Tarun Varma
Keyword(s):  
Median Filter ◽  
Median Filtering ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Noise Density ◽  
Mean Filter ◽  
Salt And Pepper ◽  
Image Details ◽  
Lower Noise ◽  
Better Than

- Inthis paper,we propose an improved median filtering algorithm. Here, we introduced salt and pepper noise for the image corruption and reconstruct original image using different filters i.e. mean, median and improved median filter. The performance of improved median filter is good at lower noise density levels.The mean filter suppresses little noise and gets the worst results.The experimental resultsshow that our improved median filter is better than previousmedian filterfor lower noise density (upto 60%). It removes most of the noises effectively while preserving image details very well.

An Improved Hybrid Median-Mean Filter Algorithm

2014 ◽  
Vol 701-702 ◽  
pp. 288-292
Author(s):  
Fang Jia ◽  
De Cheng Xu ◽  
Xin Fu
Keyword(s):  
Gaussian Noise ◽  
Median Filter ◽  
Experimental Results ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Mean Filter ◽  
Image Details ◽  
Mean Filtering

In the process of imaging, digitalization and transmission, images are generally contaminated by Gaussian noise and salt & pepper noise, which cannot be eliminated completely at the same time only by Mean filter or Median filter. Aiming at solving this problem, an improved hybrid median-mean filter algorithm based on the Improved Median Filtering (IMF) algorithm is proposed in this paper. The experimental results show that the new algorithm shows better performance than either Median filtering algorithm or Mean filtering algorithm, which can not only get rid of Gaussian noise and salt & pepper noise simultaneously, but also minimize the contradictions between noise erasing and image details protecting effectively.

An Alternate Algorithm for (3x3) Median Filtering of Digital Images

2012 ◽  
Vol 2 (1) ◽  
pp. 7-9 ◽  
Author(s):  
Satinderjit Singh
Keyword(s):  
Median Filter ◽  
Computational Cost ◽  
Spatial Coherence ◽  
General Purpose ◽  
Median Filtering ◽  
Basic Algorithm ◽  
Temporal Complexity ◽  
Filter Kernel ◽  
One Step ◽  
High Computational Cost

Median filtering is a commonly used technique in image processing. The main problem of the median filter is its high computational cost (for sorting N pixels, the temporal complexity is O(N·log N), even with the most efficient sorting algorithms). When the median filter must be carried out in real time, the software implementation in general-purpose processorsdoes not usually give good results. This Paper presents an efficient algorithm for median filtering with a 3x3 filter kernel with only about 9 comparisons per pixel using spatial coherence between neighboring filter computations. The basic algorithm calculates two medians in one step and reuses sorted slices of three vertical neighboring pixels. An extension of this algorithm for 2D spatial coherence is also examined, which calculates four medians per step.

Preliminary study of improved median filter using adaptively mask size in light microscopic image

Microscopy ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 31-36
Author(s):  
Ji-Youn Kim ◽  
Youngjin Lee
Keyword(s):  
Image Quality ◽  
Coefficient Of Variation ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Microscopic Image ◽  
Signal To Noise ◽  
Mask Size ◽  
Preliminary Study ◽  
Noise Ratio ◽  
Image Details

Abstract This study aimed to develop and evaluate an improved median filter (IMF) with an adaptive mask size for light microscope (LM) images. We acquired images of the mouse first molar using a LM at 100× magnification. The images obtained using our proposed IMF were compared with those from a conventional median filter. Several parameters such as the contrast-to-noise ratio, coefficient of variation, no-reference assessments and peak signal-to-noise ratio were employed to evaluate the image quality quantitatively. The results demonstrated that the proposed IMF could effectively de-noise the LM images and preserve the image details, achieving a better performance than the conventional median filter.

scholarly journals From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising

2020 ◽  
Author(s):  
Manfred Hartbauer
Keyword(s):  
Image Denoising ◽  
Color Images ◽  
Insect Vision ◽  
Night Vision ◽  
Primitive Type ◽  
Noise Estimate ◽  
Local Means ◽  
Static Images ◽  
Image Details ◽  
Novel Algorithm

Night active insects inspired the development of image enhancement methods that uncover the information contained in dim images or movies. Here, I describe a novel bionic night vision (NV) algorithm that operates in the spatial domain to remove noise from static images. The parameters of this NV algorithm can be automatically derived from global image statistics and a primitive type of noise estimate. In a first step, luminance values were ln-transformed, and then adaptive local means’ calculations were executed to remove the remaining noise without degrading fine image details and object contours. Its performance is comparable with several popular denoising methods and can be applied to grey-scale and color images. This novel algorithm can be executed in parallel at the level of pixels on programmable hardware.

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