KACM: A KIS-awared active contour model for low-contrast image segmentation

Abstract

Low-contrast image segmentation is a challenging task, as it requires distinguishing target objects from the background with minimal intensity differences, while also dealing with factors such as noise and blur. In recent years, the active contour model (ACM) has gained popularity for its high accuracy and efficiency in image segmentation. However, it often struggles with low-contrast images segmentation. To address this issue, we propose a KIS-awared ACM (KACM) in this paper. Firstly, based on the Koschmieder imaging system (KIS), the observed low-contrast image is modeled as a combination of the true image and two imaging factors: the scene transmission function and the global intensity of light in the imaging environment. Next, we pursue the true image in the logarithmic domain using the maximum a posteriori (MAP) criterion, and establish the active contour model. In this model, the true image follows a piecewise lognormal distribution, while the scene transmission function is described as a Markov random field (MRF), with its prior probability being defined as a Gibbs energy function. Lastly, an alternating iterative algorithm that combines variation calculus and gradient descent of the three-step time-splitting method is introduced to solve the proposed model. We validate the proposed model through qualitative and quantitative experiments, demonstrating its effectiveness in low-contrast image segmentation. Compared to several state-of-the-art models, the proposed KACM exhibits competitive performance in terms of both accuracy and efficiency.