Document Detail


Adaptive image contrast enhancement based on human visual properties.
MedLine Citation:
PMID:  18218535     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Existing methods for image contrast enhancement focus mainly on the properties of the image to be processed while excluding any consideration of the observer characteristics. In several applications, particularly in the medical imaging area, effective contrast enhancement for diagnostic purposes can be achieved by including certain basic human visual properties. Here the authors present a novel adaptive algorithm that tailors the required amount of contrast enhancement based on the local contrast of the image and the observer's Just-Noticeable-Difference (JND). This algorithm always produces adequate contrast in the output image, and results in almost no ringing artifacts even around sharp transition regions, which is often seen in images processed by conventional contrast enhancement techniques. By separating smooth and detail areas of an image and considering the dependence of noise visibility on the spatial activity of the image, the algorithm treats them differently and thus avoids excessive enhancement of noise, which is another common problem for many existing contrast enhancement techniques. The present JND-Guided Adaptive Contrast Enhancement (JGACE) technique is very general and can be applied to a variety of images. In particular, it offers considerable benefits in digital radiography applications where the objective is to increase the diagnostic utility of images. A detailed performance evaluation together with a comparison with the existing techniques is given to demonstrate the strong features of JGACE.
Authors:
T L Ji; M K Sundareshan; H Roehrig
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on medical imaging     Volume:  13     ISSN:  0278-0062     ISO Abbreviation:  IEEE Trans Med Imaging     Publication Date:  1994  
Date Detail:
Created Date:  2008-01-25     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8310780     Medline TA:  IEEE Trans Med Imaging     Country:  United States    
Other Details:
Languages:  eng     Pagination:  573-86     Citation Subset:  -    
Affiliation:
Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ.
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