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.

雾气的存在会影响需要检测图像特征的众多应用，例如目标跟踪，物体识别和基于摄像头的高级驾驶辅助系统。本文提出了一种优化框架，以有效地估计场景传输图，从而有效地帮助了除雾过程。在制定的优化模型中，引入了使用加权核范数最小化的低秩逼近，以平滑从朦胧数据中获得的粗透射图，从而避免了模糊图像中的视觉伪像。采用总变化正则化来保留透射图中的突出边缘和显着结构细节。此外，包含\（l_1 \）规范最小化通过增强微小的稀疏结构细节来帮助获得更精细的透射图，从而提供良好的除雾效果。所提出的模型的美仅限于有效优化的统一优化模型的估计，该模型用于通过精细调整的正则项估算传输图，该模型到目前为止尚未在图像去雾方向上报道。大量的实验证明，所提出的方法在图像去雾方面超越了现有技术。