Active contour image segmentation model with de‐hazing constraints. Issue 5 (9th March 2020)

• Record Type: Journal Article
• Authors: Ali, Haider; Sher, Awal; Saeed, Maryam; Rada, Lavdie
• Is Part Of: IET image processing. Volume 14:Issue 5(2020)
• Page Start: 921
• Page End: 928
• Publication Date: 2020-03-09
• Subjects: fog -- image colour analysis -- image denoising -- computer vision -- image enhancement -- image segmentation; active contour image segmentation model -- de‐hazing constraints -- hazy weather conditions -- foggy weather conditions -- atmospheric particles -- computer vision systems -- image de‐hazing -- weather factors -- image visualisation -- hazy scenes -- easy image postprocessing -- human assistance systems -- variational segmentation model -- de‐hazing constraint terms -- coupled dehazing‐segmentation model -- image structure boundaries -- image quality -- robust dehazing‐segmentation scheme -- vector‐valued images -- dehazing preprocessing; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5987
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which makes the objects and their features difficult to be identified by computer vision systems. In the past decades, image de‐hazing is used to remove the influence of weather factors and improve image visualisation in hazy scenes by providing easy image post‐processing towards human assistance systems benefit. In this study, the authors present a variational segmentation model equipped with de‐hazing constraint terms in a new coupled dehazing‐segmentation model. The proposed hybrid formulation not only recovers/restores the fog/haze degradation but at the same time segments image degraded object/objects by solving in this way the difficulties of simultaneously performed dehazing and segmentation pre/post‐processing. This combination takes into account the image structure boundaries and the image quality, leading in this way to a robust dehazing segmentation scheme. The advantages of the proposed method are the suitability of the model for grey and vector‐valued images, a small number of parameters involved, and a rather good speed of the algorithm. Experiments show that their approach outperforms the state‐of‐the‐art algorithms in terms of segmentation accuracy while avoiding a dehazing preprocessing which reflects an extended CPU time.