Fuzzy-Contextual Contrast Enhancement

This paper presents contrast enhancement algorithms based on fuzzy contextual information of the images. We introduce fuzzy similarity index and fuzzy contrast factor to capture the neighborhood characteristics of a pixel. A new histogram, using fuzzy contrast factor of each pixel is developed, and...

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Bibliographic Details
Published in:IEEE transactions on image processing 2017-04, Vol.26 (4), p.1810-1819
Main Authors: Parihar, Anil Singh, Verma, Om Prakash, Khanna, Chintan
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Brightness
Coefficient of variation
context adaptive
Contrast enhancement
Distribution functions
Experimentation
fuzzy logic
Heuristic algorithms
histogram equalization
Histograms
Image contrast
Image enhancement
Image quality
Indexes
Interferon
Pixels
Similarity
Transfer functions
Visual perception
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Summary:This paper presents contrast enhancement algorithms based on fuzzy contextual information of the images. We introduce fuzzy similarity index and fuzzy contrast factor to capture the neighborhood characteristics of a pixel. A new histogram, using fuzzy contrast factor of each pixel is developed, and termed the fuzzy dissimilarity histogram (FDH). A cumulative distribution function is formed with normalized values of an FDH and used as a transfer function to obtain the contrast enhanced image. The algorithm gives good contrast enhancement and preserves the natural characteristic of the image. In order to develop a contextual intensity transfer function, we introduce a fuzzy membership function based on fuzzy similarity index and coefficient of variation of the image. The contextual intensity transfer function is designed using the fuzzy membership function to achieve final contrast enhanced image. The overall algorithm is referred as the fuzzy contextual contrast-enhancement algorithm. The proposed algorithms are compared with the conventional and the state-of-the-art contrast enhancement algorithms. The quantitative and visual assessment of the results is performed. The results of quantitative measures are statistically analyzed using t-test. The exhaustive experimentation and analysis show the proposed algorithm efficiently enhances contrast and yields in natural visual quality images.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2017.2665975