Image dehazing algorithm by combining light field multi-cues and atmospheric scattering model

Image captured in foggy weather often exhibits low contrast and poor image quality, which may have a negative impact on computer vision applications. Aiming at these problems, we propose an image dehazing algorithm by combining light field technology with atmospheric scattering model. Firstly, taking the advantages of capturing multi-view information from light field camera is used to extracting defocus cues and correspondence cues, which are used to estimating the depth information of hazy images, and use the obtained depth information to calculating the scene’s initial transmission. Then use scene depth information to build a new weight function, and combined it with 1-norm context regularization to optimizing the initial transmission map iteratively. Finally, the central perspective image of hazy light field images is dehazed using atmospheric scattering model to obtain the final dehazed images. Experimental results on synthetic hazy images and real hazy images demonstrate that, compared to existing single image dehazing algorithms, the peak signal to noise ratio get 2 dB improvement and the structural similarity raise about 0.04. Moreover, our approach preserves more fine structural information of images and has faithful color fidelity, thus yielding a superior image dehazing result.