An intelligent framework for transmission map estimation in image dehazing using total variation regularized low-rank approximation

The presence of haze affects a multitude of applications that require detection of image features, such as target tracking, object recognition and camera-based advanced driving assistance systems. In this paper, an optimization framework is proposed to efficiently estimate the scene transmission map which aids the dehazing process in an effective manner. In the formulated optimization model, low-rank approximation using weighted nuclear norm minimization is introduced to smoothen the coarse transmission map obtained from hazy data in order to avoid the visual artifacts in the dehazed image. Total variation regularization is employed to preserve the prominent edges and salient structural details in the transmission map. Moreover, the inclusion of l(1) norm minimization helps to obtain a finer transmission map by enhancing the minute sparse structural details, thereby providing good dehazing results. The beauty of the proposed model is confined in the efficient formulation of a unified optimization model for the estimation of transmission map with fine-tuned regularization terms which is not reported until now in the direction of image dehazing. The extensive experiments prove that the proposed method surpasses the state-of-the-art methods in image dehazing.

Automated summary

An optimization framework is proposed to efficiently estimate the scene transmission map for effective dehazing, introducing low-rank approximation and total variation regularization to enhance dehazing effects. The inclusion of l(1) norm minimization enhances details, resulting in dehazing results surpassing state-of-the-art methods.