scholarly journals Enhancement of Aerial and Medical Image using Multi resolution pyramid

2011 ◽  
pp. 38-45
Author(s):  
Ramyashree N ◽  
Pavithra P ◽  
Shruthi T.V ◽  
Dr. Jharna Majumdar
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Scale Analysis ◽  
Local Enhancement ◽  
Comparative Performance ◽  
Quality Metric ◽  
Multi Scale ◽  
Active Research ◽  
Global And Local

Image enhancement has been an area of active research for decades. Most of the studies are aimed at improving the quality of image for better visualization. An approach for contrast enhancement utilizing multi-scale analysis is introduced. To show the effects of image enhancement, quantitative measures should be introduced. In this paper, we examine the effect of global and local enhancement using multi resolution pyramids. We identify a set of quality metric parameters for comparative performance analysis and use it to assess the enhanced output image for a number of image enhancement algorithms using pyramids.

Medical Image Enhancement Using Edge Information-Based Methods

2017 ◽  
pp. 123-148
Author(s):  
S. Anand
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Hyperbolic Function ◽  
Edge Information ◽  
X Ray ◽  
Enhancement Method ◽  
Chest X Ray ◽  
Medical Image Enhancement

Medical image enhancement improves the quality and facilitates diagnosis. This chapter investigates three methods of medical image enhancement by exploiting useful edge information. Since edges have higher perceptual importance, the edge information based enhancement process is always of interest. But determination of edge information is not an easy job. The edge information is obtained from various approaches such as differential hyperbolic function, Haar filters and morphological functions. The effectively determined edge information is used for enhancement process. The retinal image enhancement method given in this chapter improves the visual quality of the vessels in the optic region. X-ray image enhancement method presented here is to increase the visibility of the bones. These algorithms are used to enhance the computer tomography, chest x-ray, retinal, and mammogram images. These images are obtained from standard datasets and experimented. The performance of these enhancement methods are quantitatively evaluated.

scholarly journals The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets

2020 ◽  
Vol 9 (9) ◽  
pp. 497
Author(s):  
Haydn Lawrence ◽  
Colin Robertson ◽  
Rob Feick ◽  
Trisalyn Nelson
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Spatial Scales ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Metric ◽  
Fine Grained ◽  
Multi Scale ◽  
Feasible Study

Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI.

scholarly journals Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 128973-128990
Author(s):  
Linfeng Bai ◽  
Weidong Zhang ◽  
Xipeng Pan ◽  
Chenping Zhao
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Underwater Image ◽  
Dual Image ◽  
Global And Local

Image Contrast Enhancement Algorithm Based on GM(1,1) and Power Exponential Dynamic Decision

2017 ◽  
Vol 32 (02) ◽  
pp. 1854002 ◽  
Author(s):  
Gang Li
Keyword(s):  
Image Enhancement ◽  
Local Contrast ◽  
Local Enhancement ◽  
Contrast Level ◽  
Image Edge Detection ◽  
Image Contrast Enhancement ◽  
Image Edge ◽  
Image Enhancing ◽  
Enhancement Algorithm

Image enhancement processing is a very important operation during image preprocessing. Compared with to enhancc the overall contrast level of image, enhancing the local contrast of image can improve the level of such contrast directly as well as the quality and effect of image enhancement. In this paper, the gray prediction model is applied to the process of enhancing image local contrast, so as to measure the change range of image local contrast and adaptively adjust the scale of enhancing image local contrast. The simulation results show that, in addition to enhancing the contrast of gray level on the edge of image, the proposed algorithm can inhibit roughened nonedge region and improve the quality of local enhancement processing, which create a more favorable condition for the further image edge detection.

A New Medical Image Enhancement Method Based on Human Visual Characteristics

2014 ◽  
Vol 543-547 ◽  
pp. 2543-2546
Author(s):  
Ai Bin Dong ◽  
Yun Feng Zhang ◽  
Yi Fang Liu
Keyword(s):  
Visual System ◽  
Image Enhancement ◽  
Human Visual System ◽  
Medical Image ◽  
Region Of Interest ◽  
Enhancement Method ◽  
Visual Characteristics ◽  
Medical Image Enhancement ◽  
Human Visual Characteristics

Studying of image enhancement shows that the quality of image heavily relies on human visual system. In this paper, we apply this fact to design a new image enhancement method for medical images that improves the detail regions. First, the eye region of interest (ROI) is segmented; then the Un-sharp Masking (USM) is used to enhance the detail regions. Experiments show that the proposed method can effectively improve the accuracy of medical image enhancement and has a significant effect.

Color Medical Image Enhancement Using Y-H Model and Wavelet Homomorphic Filtering

2013 ◽  
Vol 411-414 ◽  
pp. 1020-1024
Author(s):  
Hua Liang ◽  
Zhen Tao Zhou ◽  
Hao Feng ◽  
Li Jun Ding ◽  
Ju Ping Gu ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Color Image ◽  
Color Space ◽  
Inverse Transformation ◽  
Homomorphic Filtering ◽  
Enhancement Method ◽  
Rgb Color Space ◽  
Medical Image Enhancement

Color medical images are widely used in the field of medical diagnosis. Image enhancement is one of the most important pretreatment methods which can enhance the quality of images. In this paper, a novel color image enhancement method using Y-H model and wavelet homomorhpic filtering is put forward. The chromaticity numbers matrix and intensity numbers matrix of color images are get using Young-Helmholtz (YH) transform. The chromaticity numbers matrix remains unchanged. Wavelet homomorphic filtering method is used to process intensity numbers matrix . The enhanced intensity numbers matrix and formerly chromaticity numbers matrix are processed by Y-H inverse transformation and disply in RGB color space. The method put forward in the paper is successfully used in color medical image enhancement. Experimental results show that the method have characteristics of nondistortion, better visual effect.

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