Variational Regularized Transmission Refinement for Image Dehazing
This addresses the problem of producing high-quality dehazed images for computer vision applications, but it is incremental as it builds on existing transmission estimation techniques.
The paper tackled image dehazing by refining transmission maps through a hybrid variational model with promoted regularization, achieving competitive or superior performance compared to state-of-the-art methods on synthetic and realistic images.
High-quality dehazing performance is highly dependent upon the accurate estimation of transmission map. In this work, the coarse estimation version is first obtained by weightedly fusing two different transmission maps, which are generated from foreground and sky regions, respectively. A hybrid variational model with promoted regularization terms is then proposed to assisting in refining transmission map. The resulting complicated optimization problem is effectively solved via an alternating direction algorithm. The final haze-free image can be effectively obtained according to the refined transmission map and atmospheric scattering model. Our dehazing framework has the capacity of preserving important image details while suppressing undesirable artifacts, even for hazy images with large sky regions. Experiments on both synthetic and realistic images have illustrated that the proposed method is competitive with or even outperforms the state-of-the-art dehazing techniques under different imaging conditions.