A software supported image enhancement approach based on DCT and quantile dependent enhancement with a total control on enhancement level
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In many computer vision applications like medical imaging, pattern recognition etc., image enhancement is an important pre-processing requirement which is used to improve the efficiency of an application. A significantly large literature is available on image enhancement; unfortunately, most of these schemes have certain shortcomings for e.g. the lack of control over the contrast starching, noise enhancement and ‘mean-shift’ problem etc. To deal with the aforementioned problems, this study suggests an efficient method which is based on discrete cosine transformation (DCT) and quantile dependent sub-division of the histogram of given input image. In the proposed method, we apply DCT on the input image to get low-frequency component (LFC) and then use the quantile-based sub-division on the histogram of LFC. Finally, histogram equalization is performed on all these sub-histograms separately. The main advantage of quantile-based segmentation is that here entire intensity spectrum participates in the enhancement process, which provides a total control over the enhancement level. In the proposed method the high-frequency component remains untouched and hence the structural information of the input image and the noise in the input image remains unaffected by the image enhancement process.
KeywordsDiscrete cosine transform Contrast enhancement Linearly quantile separated histogram equalization Greyscale images Mean-shift problem
Authors would like to thanks to all the reviewers for their useful suggestion and precious time for reviewing this work. Their suggestion help us a lot to improve the quality of the paper. Also we are grateful to the editor associated with this paper for his/her cooperation and support.
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