On generalized Sugeno’s class generator and parametrized intuitionistic fuzzy approach for enhancing low-light images

Abstract

Enhancing low-light images poses a significant challenge in terms of pixel distortion, color degradation, detail loss, over enhancement and noise amplification, particularly in images that have both low light and normal light region. In recent years, researchers have increasingly turned their attention to intuitionistic fuzzy set based approaches for low light image enhancement due to their flexibility in the representation of a pixel. In this work, the generalized Sugeno’s class of generating function is proposed. Since the parameter value in the existing generating functions lies in an unbounded interval, it is difficult to find the best parameter value. By using the proposed generalized version, a few intuitionistic generating functions are analyzed where the parameter value lies in a bounded interval. A searching algorithm is also proposed to find the parameter value that maximizes the entropy of an image for any membership and generating function. Regardless of the number of decimals, the proposed approach finds the best parameter value iteratively. Then, in HSI color space, an enhancement model is designed utilizing the intuitionistic fuzzy image achieved using best parameter value and contrast-limited adaptive histogram equalization. The proposed method performs better compared to the state-of-the-art models. Also, seven image quality mathematical metrics — entropy, SSIM, correlation coefficient $\left(r\right)$, PSNR, AMBE, number of edge pixels $\left(N_g\right)$ and the fitness function are implemented to compare the proposed and state-of-the-art models.