Abstract
Low-contrast images are viewed with obscured details and are unfavorable to the observer. Hence, it is a necessity to process such an effect efficiently to get images with lucid details as the need for clear images become a global demand. Therefore, a statistics-based algorithm of simple complexity is introduced in this research to process color and grayscale images with low contrast. The proposed algorithm consists of five stages, where the first and second stages include the use of two different statistical s-curve transformations, the third stage combines the outputs of the aforesaid stage, the fourth stage improves the brightness, and the fifth stage reallocates the pixels to the natural interval. The proposed algorithm is compared with six modern algorithms, and the outputs are evaluated using two no-reference methods. The obtained results show that the proposed algorithm performed the best, providing the highest image evaluation readings and it was the fastest among the comparison methods.
