Identification for the Low-Contrast Image Signal with Regularized Variational Term and Dynamical Saturating Nonlinearity

• Published in: Journal of systems science and complexity Vol. 36; no. 3; pp. 1089 - 1102
• Main Authors: Zhang, Ning, Ma, Yumei, Pan, Zhenkuan, Huang, Baoxiang, Wang, Dongcheng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023; Springer Nature B.V
• Subjects: Color imagery; Complex Systems; Control; Image contrast; Image enhancement; Image processing; Image quality; Mathematics; Mathematics and Statistics; Mathematics of Computing; Model testing; Nonlinearity; Operations Research/Decision Theory; Statistics; Stochastic resonance; Systems Theory; image enhancement; regularized variational term; stochastic resonance (SR); Dynamical saturating nonlinearity; low contrast
• ISSN: 1009-6124; 1559-7067
• DOI: 10.1007/s11424-023-1270-5

In recent years, image processing based on stochastic resonance (SR) has received more and more attention. In this paper, a new model combining dynamical saturating nonlinearity with regularized variational term for enhancement of low contrast image is proposed. The regularized variational term can be setting to total variation (TV), second order total generalized variation (TGV) and non-local means (NLM) in order to gradually suppress noise in the process of solving the model. In addition, the new model is tested on a mass of gray-scale images from standard test image and low contrast indoor color images from Low-Light dataset (LOL). By comparing the new model and other traditional image enhancement models, the results demonstrate the enhanced image not only obtain good perceptual quality but also get more excellent value of evaluation index compared with some previous methods.