scholarly journals An Adaptive Weighted Image Denoising Method Based on Morphology

2021 ◽  
Vol 15 ◽  
pp. 271-279
Author(s):  
Jinjuan Wang ◽  
Shan Duan ◽  
Qun Zhou
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Mathematical Morphology ◽  
Edge Preservation ◽  
Denoising Method ◽  
Edge Information ◽  
Processing Step ◽  
Blurred Image ◽  
Image Edge ◽  
Cascaded Filter

In its generation, transmission and record, image signal is often interfered by various noises, which have severally affected the visual effects of images; therefore, it is a very important pre-processing step to take proper approaches to reduce noises. Conventional denoising methods have also blurred image edge information while removing noises, which can be overcome by the method based on mathematical morphology. While eliminating different noises from images, it can not only keep clear object edges, but also preserve as many image details as possible and it also has excellent capacities in noise resistance and edge preservation. With image denoising and mathematical morphology as the research subject, this paper analyzes the generation and characteristics of common image noises, studies the basic theories of mathematical morphology and its applications in image processing, discusses the method to select structural elements in mathematical morphology and proposes a filtering algorithm which combines image denoising and mathematical morphology. This method conducts morphological filtering and denoising on noised image with filter cascade and its performance is verified with stimulation testing. The experiment results prove that the approach to build the morphological filter into cascaded filter through series and parallel connection can to a certain extent, affect the effect of common filter while being applied to different image processing.

Image denoising method used in earthquake ruin scene based on multi-scale mathematical morphology

2012 ◽  
Vol 10 (s2) ◽  
pp. S21002-321004 ◽  
Author(s):  
Yanxing Song Yanxing Song ◽  
Shucong Liu Shucong Liu ◽  
Jingsong Yang Jingsong Yang
Keyword(s):  
Image Denoising ◽  
Mathematical Morphology ◽  
Denoising Method ◽  
Multi Scale

Comparison and Improvements of Image Denoising Based on Wavelet Transform

2015 ◽  
Vol 740 ◽  
pp. 644-647
Author(s):  
Xue Mei Xiao
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Image Denoising ◽  
Wavelet Denoising ◽  
Important Application ◽  
Denoising Method ◽  
Advantages And Disadvantages ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

Wavelet transform denoising is an important application of wavelet analysis in signal and image processing. Several popular wavelet denoising methods are introduced including the Mallat forced denoising, the wavelet transform modulus maxima method and the nonlinear wavelet threshold denoising method. Their advantages and disadvantages are compared, which may be helpful in selecting the wavelet denoising methods. At the same time, several improvement methods are offered.

Based on Local Structural Similarity Image Denoising Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3313-3317
Author(s):  
Ze Hua Zhou ◽  
Wei Tang Zhang
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Information Source ◽  
Structural Similarity ◽  
Redundant Information ◽  
Research Focus ◽  
Edge Information ◽  
Search Area ◽  
Computer Image Processing ◽  
And Storage

The image is a kind of important information source, through the image processing can help people understand the connotation of the information. However, the image in the process of generation and transmission by all kinds of the noise, the information processing, transfer and storage caused tremendous influence. So the image denoising always all is the computer image processing and computer in the vision of a research focus. Proposed an algorithm of image denoising based on the local structural similarity. Which utilizes the redundant information, and by establishing a similar function to the search area calculation of point and to pixels similarity of weights, and then to the search area at the weighted, obtained the last to pixels gray value varies. This algorithm in texture, of the edge information denoising ways than the current many denoising algorithm are excellent.

An Automotive Airbag Detection Method Based on Image Processing

2013 ◽  
Vol 416-417 ◽  
pp. 1350-1354 ◽  
Author(s):  
Xiang Xin Shao ◽  
Mu Jun Xie
Keyword(s):  
Image Processing ◽  
Automobile Industry ◽  
Detection Method ◽  
Detection Methods ◽  
Edge Information ◽  
Invariant Moment ◽  
Boundary Tracking ◽  
Image Edge ◽  
Auto Parts ◽  
Dial Indicator

The development of the automobile industry led to the development of auto parts, and airbags are one of the most important safety components of cars. In this paper, to meet the shortfall of traditional way of using a dial indicator to detect airbags deficiencies, a new automotive airbag shape detection methods based on image processing technology is put forward. First, extract the airbags image edge information using the method of boundary tracking, then detect whether airbags are qualified according to the similarity of image invariant moment. Experiments confirmed this method can improve the range and accuracy of the airbag detection.

A Contour Tracing Algorithm for Ginseng Shape

2013 ◽  
Vol 401-403 ◽  
pp. 1268-1271
Author(s):  
Bing Quan Huo ◽  
Feng Ling Yin
Keyword(s):  
Image Processing ◽  
Computer Technology ◽  
Color Image ◽  
Binary Image ◽  
Color Information ◽  
Edge Information ◽  
Boundary Information ◽  
Image Edge ◽  
Tracing Algorithm ◽  
Neighbourhood Algorithm

More and more applications of computer technology are used in the field of agriculture. In this paper, Image processing technology is applied to ginseng shape. Get a color image from the device, remove the color information and reserve the boundary information. Highlight the image edge information by gradient sharpening, binary image and use 8-neighbourhood algorithm for tracking the border.

Extraction of Image with Complex Noise Based on Wavelet Analysis and Morphology

2014 ◽  
Vol 556-562 ◽  
pp. 5081-5084
Author(s):  
Xing Yan Hua
Keyword(s):  
Image Processing ◽  
Wavelet Analysis ◽  
Mathematical Morphology ◽  
Operation Time ◽  
Spatial Filter ◽  
New Method ◽  
Wavelet Filter ◽  
Noise Resistance ◽  
Image Edge ◽  
Edge Based

The spatial filter and wavelet filter were used to denoise the image with much complex noise. The mathematical morphology and threshold segmentation were integrated to detect the image edge. Based on comparison between the method given in this paper and the traditional methods, the new method can result in satisfying image processing result. The detecting precision is high. Also, the noise resistance is very good. The detected edge outlines are continuous, smooth and integrated. Moreover, the operation time is less.

Multi-Dimension and Structure Element Edge-Detection Based on Mathematical Morphology

2012 ◽  
Vol 490-495 ◽  
pp. 919-921
Author(s):  
Ying Bo Liang ◽  
Li Hong Zhang
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Detection Algorithm ◽  
Edge Information ◽  
Morphological Gradient ◽  
Ideal Image ◽  
Open Operation ◽  
Image Edge ◽  
Image Details ◽  
The Ideal

A novel multi-dimension and structure element edge-detection based on mathematical morphology is presented to resolve blur problem of classical morphology when detecting an edge to reduce the noise but hard to preserve the details and edge information of the original image effectively. First,pretreatment of the image are completed by close-open operation to eliminate noise; second,do close operation to smooth image,in the end,using the operation of morphological gradient for smooting image,the ideal image edge under the environment of existing noise is obtained,and it is applied to detect the edges of welding pore images. The experimental results show that it is compared with classical Sobel operators,Canny operators and traditional edge detection algorithm, the proposed algorithm has the following distinguished advantages:accuracy of edges detected, a clear outline of the image, and can preserve more image details as well, and insensitive to noise.

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