Adaptive Low-Light Image Enhancement Using Bipolar Fuzzy Set

Digital images captured undera low-light environment often struggle to clearly assign the intensity due to uncertainty and insufficient illumination. To address such issues, fuzzy set theory plays a crucial role. Bipolar fuzzy set, an important extension of the conventional fuzzy set, provides an advanced framework to deal with uncertainty by focusing on positive and negative membership grades. However, handling negative membership grades and developing an image enhancement model that accesses bipolar fuzzy information pose significant challenges. To address this issue, the present study designs a bipolar fuzzy set-based low-light image enhancement model by leveraging the one-to-one correspondence between bipolar fuzzy set and two-polar fuzzy set. In addition, an image fusion approach is employed to combine the images of positive and negative membership grades. Finally, the experimental study revealed that the proposed model is superior to several state-of-the-art techniques in terms of both enhancement quality and computational efficiency.