scholarly journals Application of Different Filtering Techniques in Digital Image Processing

2021 ◽  
Vol 2062 (1) ◽  
pp. 012007
Author(s):  
Sumant Sekhar Mohanty ◽  
Sushreeta Tripathy
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Noise ◽  
Noise Model ◽  
Electronic Noise ◽  
Original Image ◽  
Color Information ◽  
Processing Steps ◽  
Filtering Techniques

Abstract Noise in an image is a random variation of brightness or color information in the original image. Noise is consistently presented in digital images during picture obtaining, coding, transmission, and processing steps. Image noise is most apparent in image regions with a low signal level. There are various reasons for the creation of noise in an image, such as electronic noise in amplifiers or detectors, disturbances and overheating of the sensor, disturbances in the medium of traveling for a digital image, etc. Noise is exceptionally hard to eliminate from the digital pictures without the earlier information of the noise model. There are various types of noise that can be available in a noise model. Filters are used to remove these types of noises in a digital image in image processing. In this research, we have implemented different filtering techniques that have been used to remove the noises in an image.

Comparative Analysis on Noise Processing of Image

2013 ◽  
Vol 718-720 ◽  
pp. 2159-2162
Author(s):  
Hua Jun Dong ◽  
Xue Mei Jiang ◽  
Chen Xu Niu
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Digital Image Processing ◽  
Digital Image ◽  
Median Filter ◽  
Wiener Filter ◽  
Image Noise ◽  
Two Dimensional ◽  
Wavelet Filter ◽  
Average Filter

The existence of noises have great interference on image processing, so the elimination of image noise is of great importance. In this paper, based on the digital image processing, the methods of average filter, wiener filter, median filter, two-dimensional wavelet filter, maximum and minimum filter are used to eliminate the salt & pepper noise of image. Then we analysis and compare the results of the five methods to find the best way to eliminate the image noise.

Applied-Information Technology with Method of Digital Image Noise Processing

2014 ◽  
Vol 951 ◽  
pp. 253-256 ◽  
Author(s):  
Guo Qing Yu ◽  
Chen Meng Sui ◽  
Bing Dong Sui
Keyword(s):  
Image Processing ◽  
Information Technology ◽  
Digital Image Processing ◽  
Digital Image ◽  
Average Method ◽  
Image Noise ◽  
Computer Algorithm ◽  
Basic Knowledge ◽  
Median Filtering ◽  
Low Pass

Digital image processing is a kind of technology which employs a certain algorithm to realize image processing through computer algorithm. The powerful capacity of computing and graphics displaying function of Matlab makes image processing becomes more simple and intuitive. This paper introduces the basic knowledge of dealing with noise in digital image, expounds the neighborhood average method, median filtering in detail, and low pass filtering and other typical of eliminating noise method , and at the same time analyzes and compares the characteristics of several typical methods by use of the software Matlab.

The Digital Method of Clothing Extension Measurement

2011 ◽  
Vol 175-176 ◽  
pp. 807-810
Author(s):  
Zhi Ge Chen ◽  
Qing Guan Chen
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Noise ◽  
Digital Method ◽  
Stripe Width ◽  
Fabric Strength ◽  
The Relationship

Fabric extension can be measured by using fabric strength tester in laboratory. But the extension is difficult to be measured when the clothing is in wearing. In this paper, the relationship between the extension of clothing and the fabric tension was discussed according to the experiment result. The digital image processing will be used to grab clothing images and to measure the extension of fabric. The techniques of digital image processing, such as binary conversion of image, noise points deleting, smooth processing of stripe edge and the stripe width measuring, were applied in this research. Based on experiment results, the relation between the fabric tension and extension were linear when the clothing was under the condition of small strength.

scholarly journals SEGMENTASI AREA GIGI MENGGUNAKAN FUZZY C-MEANS

2019 ◽  
Vol 4 (2) ◽  
pp. 75-82
Author(s):  
Hardian Oktavianto ◽  
Izzati Muhimmah ◽  
Taufiq Hidayat
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Fuzzy C Means ◽  
Identification Stage ◽  
Segmentation Image ◽  
Entire Dataset ◽  
Processing Steps ◽  
Background Area

Researches with early detection of caries using x-ray topic has been widely developed, generally before doing object detection, the early step is segmentation. Image segmentation is one of the digital image processing steps used to segregate an area or object observed with other areas or objects. Segmentation has an important role as the initial determination of the desired area or object so that it can be continued to the identification stage. FCM (fuzzy c-means) algorithm is one of object segmentation technique or object grouping in the field of digital image processing study. The basic concept of FCM is to determine the centroid and members of each group adaptively, in principle FCM uses a fuzzy grouping model so that a data or element becomes a member of all the clusters that are formed. Segmentation of the dental area using FCM with 4 clusters aims to segment the enamel, dentin, pulp, and backround areas. The result of segmentation using FCM is influenced by the condition of the dataset used. The background area of the entire dataset can be well segmented. FCM is also capable of segmenting the enamel area but in some datasets, the enamel segmentation results are still mixed with other teeth areas. For the dentin and pulp areas, the segmentation result of these two areas is not optimal yet; most of the dentin and or pulp areas are still segmented with the other teeth’s area.

scholarly journals Design a model of Image Restoration using AI in Digital Image Processing

2021 ◽  
Vol 12 (5) ◽  
pp. 862-865
Author(s):  
Boosi Shyamala, Dr. Chetana Tukkoji, Archana S Nadhan, Dioline Sara
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Digital Image Processing ◽  
Digital Image ◽  
Motion Blur ◽  
Original Image ◽  
Source Imaging ◽  
Point Extension ◽  
Noise Image ◽  
Blurred Image

Image restoration is the process of obtaining a distorted/noise image and giving an approximate clear image of the original image. False focus, motion blur and noise are forms of distortion. Image restoration can be done by reversing the process called Point Extension Function (PSF). In this process, the blurred image is generated by point source imaging and can be used to restore the image lost due to the blur process. Like to form. Modern artificial intelligence (AI) applied to image processing includes facial recognition, object recognition and detection, video, image action, and visual search. It helps to develop smart applications in digital image processing.

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

A method to measure pores and fissures in geologic materials under S.E.M. by digital image processing

1978 ◽  
Vol 36 (1) ◽  
pp. 212-213
Author(s):  
L. Montoto ◽  
M. Montoto ◽  
A. Bel-Lan
Keyword(s):  
Image Processing ◽  
Optical Microscopy ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Processing ◽  
Free Surfaces ◽  
Geologic Materials ◽  
Automatic Information ◽  
Detector Output ◽  
Rock Specimens

INTRODUCTION.- The physical properties of rock masses are greatly influenced by their internal discontinuities, like pores and fissures. So, these need to be measured as a basis for interpretation. To avoid the basic difficulties of measurement under optical microscopy and analogic image systems, the authors use S.E.M. and multiband digital image processing. In S.E.M., analog signal processing has been used to further image enhancement (1), but automatic information extraction can be achieved by simple digital processing of S.E.M. images (2). The use of multiband image would overcome difficulties such as artifacts introduced by the relative positions of sample and detector or the typicals encountered in optical microscopy.DIGITAL IMAGE PROCESSING.- The studied rock specimens were in the form of flat deformation-free surfaces observed under a Phillips SEM model 500. The SEM detector output signal was recorded in picture form in b&w negatives and digitized using a Perkin Elmer 1010 MP flat microdensitometer.

Digital image processing methods applied to the study of annealing time on semiconductor-metal eutectic composites

1988 ◽  
Vol 46 ◽  
pp. 848-849
Author(s):  
J. Hefter
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Electronic Device ◽  
Processing System ◽  
Average Diameter ◽  
Eutectic Solidification ◽  
Metal Silicide ◽  
The Matrix ◽  
Microscopic Studies

Semiconductor-metal composites, formed by the eutectic solidification of silicon and a metal silicide have been under investigation for some time for a number of electronic device applications. This composite system is comprised of a silicon matrix containing extended metal-silicide rod-shaped structures aligned in parallel throughout the material. The average diameter of such a rod in a typical system is about 1 μm. Thus, characterization of the rod morphology by electron microscope methods is necessitated.The types of morphometric information that may be obtained from such microscopic studies coupled with image processing are (i) the area fraction of rods in the matrix, (ii) the average rod diameter, (iii) an average circularity (roundness), and (iv) the number density (Nd;rods/cm2). To acquire electron images of these materials, a digital image processing system (Tracor Northern 5500/5600) attached to a JEOL JXA-840 analytical SEM has been used.

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