Fusion-based image de-fogging using dual tree complex wavelet transform

Images captured in foggy weather are severely degraded, which influences the tracking and recognition of objects present in those images. Therefore, restoring the true scene using a foggy image is important. In this paper, an effective fusion-based foggy image restoration technique by using dual tree complex wavelet transform (DT-CWT) has been proposed. Minimum color channel and the dark channel of a foggy image are constructed. Low and high pass components of both these channels are fused to obtain a transmission map. Dark channel is estimated by minimum preserving down sampling approach which improves the computational efficiency of the de-fogging process. Since DCP-based defogging techniques suffer from halo artifacts and darkness, proposed technique improves the overall contrast and the halo artifact regions in a time efficient way. To make the defogging results look uniformly bright, an adaptive post processing technique is applied on the de-fogged images. Comparative experiments with existing state-of-the-art algorithms show that de-fogging results of the proposed technique are better.