A low-light video enhancement approach using novel intuitionistic fuzzy generator

Enhancing low-light videos is essential for making them clearer and useful for various uses, including security and self-driving cars, entertainment, etc. The process of improving the contrast of low-light video is one of the significant issues due to uncertain problems such as over-contrast, poor illumination, high noise, etc. Therefore, the main objective of this research is to enhance low-light videos via a novel intuitionistic fuzzy generator (IFG). In this process, a dark video is converted into frames to get the normalized frames. The proposed novel IFG is applied to initial enhancement of the frames. Moreover, the histogram equalization technique is utilized to further enhancement of the frames. Finally, the enhanced frames are obtained by finding optimal enhanced frames using entropy measure and are compiled to convert the frames into an enhanced video. Furthermore, a comparative analysis is performed with existing approaches based on the quality measures such as entropy, peak signal-to-noise ratio and structural similarity index measure, contrast, correlation, energy and homogeneity. Also, the numerical simulations of the proposed technique are providing the optimal value of all metrics.