Partitioning hazy images using interactive active contour models

Abstract

Image partitioning, also known as image segmentation, is a process that involves dividing an image into distinct and meaningful segments. Recently, an interactive active contour model (ACM) namely the Gaussian regularization selective segmentation (GRSS) was designed to handle images with intensity inhomogeneity effectively. However, the GRSS model shows limited performance when applied to hazy images, which often results in incomplete detection and inaccurate extraction of the target object. This study reformulates the GRSS model by integrating the simple dark channel prior (SimpleDCP) dehazing technique, producing a modified model referred to as GRSS with SimpleDCP (GRSSD). The model is derived and implemented in MATLAB software. Experimental results show that the GRSSD model achieves improved segmentation accuracy (ACU) compared with the GRSS model. On average, the ACU improved by 1.8%, while the error metric (EM) decreased from 0.053 to 0.036, representing a reduction of about 32%. The Dice and Jaccard indices improved by approximately 2.6% and 4.9%, respectively. Although the computation time increased, the enhancement in segmentation ACU demonstrates the benefit of incorporating a dehazing process into the variational formulation. The proposed GRSSD model can be extended to color and three-dimensional image segmentation in future work.