Abstract
Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of transmission map plays a vital role in image dehazing. In this work, the coarse transmission map is firstly estimated using a robust fusion-based strategy. A unified optimization framework is then proposed to estimate the refined transmission map and latent sharp image simultaneously. The resulting constrained minimization model is solved using a two-step optimization algorithm. To further enhance dehazing performance, the solutions of subproblems obtained in this optimization algorithm are equivalent to deep learning-based image denoising. Due to the powerful representation ability, the proposed method can accurately and robustly estimate the transmission map and latent sharp image. Numerous experiments on both synthetic and realistic datasets have been performed to compare our method with several state-of-the-art dehazing methods. Dehazing results have demonstrated the proposed method’s superior imaging performance in terms of both quantitative and qualitative evaluations. The enhanced imaging quality is beneficial for practical applications in maritime ITS, for example, vessel detection, recognition, and tracking.
Highlights
Maritime video surveillance system has always been an indispensable part of maritime supervision
Since haze is generated in the maritime environment and seriously affects the visual effect, it is necessary to research the dehazing of maritime images
We firstly select 500 depth maps from the NYU Depth dataset, use equation (17) to transform them into corresponding transmission maps, and synthesize transmission maps with different noise levels according to equation (17). en, these synthesized images and their source images are cropped into many image blocks whose sizes are 128 × 128
Summary
Maritime video surveillance system has always been an indispensable part of maritime supervision. Since haze is generated in the maritime environment and seriously affects the visual effect, it is necessary to research the dehazing of maritime images. By proposing a new prior named color attenuation prior, Zhu et al [6] created a linear model to estimate the scene depth under the new prior. E algorithm’s defect lies in that the estimation of the scene depth of hazy white images is biased, affecting the dehazing effect. Many variationsbased image dehazing methods have been proposed [7,8,9,10]. These methods can perform well in some situations, they cannot robustly process maritime images due to huge texture structure differences
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