Adaptive Low-Light Image Enhancement Using Bipolar Fuzzy Set

TY - JOUR

T1 - Adaptive Low-Light Image Enhancement Using Bipolar Fuzzy Set

AU - Mahesh Kumar, C. V.

AU - Nithyanandham, Deva

AU - David Raj, M.

AU - Augustin, Felix

AU - Ramasamy, Saravanakumar

AU - Saraswathi, D.

AU - Zhang, Ye

N1 - Publisher Copyright: © 1993-2012 IEEE.

PY - 2026/5/1

Y1 - 2026/5/1

N2 - 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.

AB - 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.

KW - Bipolar fuzzy set (BFS)

KW - fuzzy complement

KW - low-light image enhancement (LLIE)

KW - negative membership function

KW - two-polar fuzzy set

UR - https://www.scopus.com/pages/publications/105031455076

U2 - 10.1109/TFUZZ.2026.3666002

DO - 10.1109/TFUZZ.2026.3666002

M3 - Article

AN - SCOPUS:105031455076

SN - 1063-6706

VL - 34

SP - 1600

EP - 1614

JO - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

IS - 5

ER -