Fusion-based image de-fogging using dual tree complex wavelet transform
Isha Kansal, Singara Singh Kasana · International Journal of Wavelets Multiresolution and Information Processing · 2018
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 de-fogging 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 de-fogging 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.