scholarly journals Experimental Study of Modified Multilevel Median Filter for Noise Reduction

2019 ◽  
pp. 135-139 ◽  
Author(s):  
Eko Hariyanto ◽  
Andysah Putera Utama Siahaan ◽  
Solly Aryza
Keyword(s):  
Experimental Study ◽  
Noise Reduction ◽  
Image Enhancement ◽  
Digital Image ◽  
Digital Images ◽  
Median Filter ◽  
Experiment Study ◽  
Digital Image Enhancement

Digital image enhancement is efforts to improve the quality of a declining image and one of the causes of the decline in the quality of digital images is the emergence of spots called noise. Median filter is one method that is widely used and developed to digital images noise reduction. In this paper, we conducted an experiment study to reduce noise using a standard multilevel median filter and a modified multilevel median filter. Further, we measured the images filtered quality using MSE and PNSR to find out the advantages of both methods.

scholarly journals A Case Study to Improve the Quality of Median Filter

2021 ◽  
Vol 10 (11) ◽  
pp. 19-28
Author(s):  
Ziad A. Alqadi ◽  
Mohamad Tariq Barakat
Keyword(s):  
Noise Reduction ◽  
Digital Image ◽  
Median Filter ◽  
Reduction Process ◽  
Research Paper ◽  
Quality Factors ◽  
High Noise ◽  
Negative Effect

The median filter is used to reduce the effect of noise, but it treats all pixels, whether they are noise points or not, which negatively affects many non-noise values in the digital image, and the negative effect increases as the noise ratio increases. In order to get rid of some of the disadvantages of the median filter, we will present in this research paper a detailed study that works on treating the unaffected and infected pixels so that this treatment leads to improving the performance of the filter by raising the values of the quality factors of the filter. The improvements added to the median filter will raise the efficiency of the noise reduction process, especially for high noise ratios.

Digital Image Processing

2018 ◽  
pp. 9-43
Keyword(s):  
Image Processing ◽  
Quality Improvement ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Images ◽  
Visual Quality ◽  
Spatial Filtering ◽  
Digital Image Enhancement ◽  
Java Code

The first two sections of this chapter introduce the motivation and fundamental theoretical perceptions towards DIP and encoding formats. The RGB system color and the visual quality improvement (by increasing the contrast with histograms enhancement techniques) are emphasized in section “Digital Image Enhancement” and “Elementary Operations With Digital Images”, respectively. Spatial filtering and masks are analyzed in section “Filtering”, which some Java code has been included for illustrative purposes. The next section explains how to save a myGeoffice© generated image to your hard disk and provides examples of image processing. Section “JHLABS® Image Editor” and “LightBox® Image Editor” depicts JHLabs®, a Web editor built on Swing technology, and Lightbox® of myGeoffice©. Its filtering capabilities are presented in the last section.

Structure of the regular surface layer of Bacillus polymyxa

1983 ◽  
Vol 29 (7) ◽  
pp. 775-780 ◽  
Author(s):  
S. K. Burley ◽  
R. G. E. Murray
Keyword(s):  
Surface Layer ◽  
Image Enhancement ◽  
Digital Image ◽  
General Scheme ◽  
Bacillus Polymyxa ◽  
Surface Layers ◽  
Regular Surface ◽  
Protein Subunits ◽  
Digital Image Enhancement ◽  
Spore Forming Bacteria

The structure of the regular surface layer of Bacillus polymyxa has been examined to a resolution of 2.5–3.0 nm in electron micrographs of negatively stained preparations supplemented by optical-digital image enhancement. The layer is composed of morphological units, each consisting of four identical protein subunits, and has p4 symmetry with a = 10.0 nm. Within each unit cell are areas of high stain density of a limiting diameter of 2–3 nm, which are interpreted as holes penetrating the layer. A comparison is made with other tetragonal regular surface layers of spore-forming bacteria, B. sphaericus and Sporosarcina ureae, solved to 2.5-nm resolution. These layers are similar in the conformation of their protein units and in the distribution of the holes in the layer. A general scheme for the architecture of the tetragonal regular surface layers is derived.

Digital image enhancement for in vivo laser scanning microscopy

2005 ◽  
Vol 11 (4) ◽  
pp. 248-253 ◽  
Author(s):  
Martin J. Gruber ◽  
Alexandra Wackernagel ◽  
Erika Richtig ◽  
Silvia Koller ◽  
Helmut Kerl ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Digital Image ◽  
Laser Scanning ◽  
Laser Scanning Microscopy ◽  
Scanning Microscopy ◽  
Digital Image Enhancement

scholarly journals Image enhancement in wavelet domain based on histogram equalization and median filter

2021 ◽  
Author(s):  
Haval Sulaiman Abdullah ◽  
◽  
Firas Mahmood Mustafa ◽  
Atilla Elci ◽  
◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Discrete Wavelet Transform ◽  
Image Enhancement ◽  
Median Filter ◽  
Histogram Equalization ◽  
Image Noise ◽  
Discrete Wavelet ◽  
Wavelet Domain ◽  
Image Noise Reduction

During the acquisition of a new digital image, noise may be introduced as a result of the production process. Image enhancement is used to alleviate problems caused by noise. In this work, the purpose is to propose, apply, and evaluate enhancement approaches to images by selecting suitable filters to produce improved quality and performance results. The new method proposed for image noise reduction as an enhancement process employs threshold and histogram equalization implemented in the wavelet domain. Different types of wavelet filters were tested to obtain the best results for the image noise reduction process. Also, the effect of canceling one or more of the high-frequency bands in the wavelet domain was tested. The mean square error and peak signal to noise ratio are used for measuring the improvement in image noise reduction. A comparison made with two related works shows the superiority of the methods proposed and implemented in this research. The proposed methods of applying the median filter before and after the histogram equalization methods produce improvement in performance and efficiency compared to the case of using discrete wavelet transform only, even with the cases of multiresolution discrete wavelet transform and the cancellation step.

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