A Novel Fuzzy Clustering-Based Histogram Model for Image Contrast Enhancement
| Autor: | Syed Shahnawazuddin, Ayur Kumar Meena, Ashish Kumar Bhandari |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Fuzzy clustering
business.industry Computer science Applied Mathematics media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Division (mathematics) ComputingMethodologies_PATTERNRECOGNITION Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering Histogram 0202 electrical engineering electronic engineering information engineering Discrete cosine transform Contrast (vision) 020201 artificial intelligence & image processing Artificial intelligence Cluster analysis business Image histogram Histogram equalization media_common |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 28:2009-2021 |
| ISSN: | 1941-0034 1063-6706 |
| Popis: | Histogram equalization is a famous method for enhancing the contrast and image features. However, in few cases, it causes the overenhancement, and hence demolishes the natural display of the image. Therefore, in this article, a new fuzzy clustering based subhistogram scheme using discrete cosine transform (DCT) for contrast enhancement has been proposed. For preserving the distinctive appearance of the image, histogram division and separate histogram equalization is done on each subhistogram. The way of dividing histogram and calculating the numbers of parts for histogram division are the major problems which directly affects the quality of the output image. The proposed fuzzy-DCT scheme includes automatic calculation of a number of parts in which histogram is divided. Histogram division has done on the basis of density function and histogram separation is computed in such a way that each main peak can be divided in a different segment. The proposed scheme consists of four stages. The first stage includes the automatic calculation of number of clusters for image brightness levels. The second stage includes clustering of brightness levels by the fuzzy c -means clustering method and utilizing the given transfer function of histogram equalization. In the third stage, contrast enhancement is computed on each individual cluster separately. In the final stage, DCT is employed on the resulting image of the third step for better contrast and brightness preservation. The simulation results of the proposed scheme reveal not only clearer features along with a contrast enhancement, but also remarkably more natural look in the images. |
| Databáze: | OpenAIRE |
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