Image enhancement by linear regression algorithm and sub-histogram equalization

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The present paper focuses on the contrast enhancement of an image using linear regression-based recursive sub-histogram equalization. The histogram of an image is partitioned into two non-overlapping sub-histograms using the mean intensity of the image. A set of points is constructed for each sub-histogram, considering gray level (intensity) as the abscissa and its corresponding count as the ordinate of the point. Then the method of least squares is used for fitting lines of regression for these sets of points in each sub-histogram. With the help of the regression line and histogram, intervals are created in each segmented partition. This process of creating intervals gives more intervals as compared to the Recursive Sub-Image Histogram Equalization (RSIHE) and the Mean and Variance-based Sub Image Histogram Equalization methods (MVSIHE). For qualitative and quantitative analysis of the proposed method, the experiments are performed on a set of test images, including medical and non-medical images. The evaluated results are presented in terms of various evaluation metrics. For medical images, the mean opinion score is also evaluated with the proposed method and other recent methods. The comparison with state-of-the-art methods shows the efficacy of the proposed method for enhancement.

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Multimedia Tools and Applications

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