Image defogging algorithm combining SLIC super-pixel segmentation and transmission optimization

When images are captured under hazy conditions, light is attenuated and deflected by particle scattering, resulting in reduced brightness and color distortion, which affects the imaging quality of the visual system. This paper proposes a defogging method that combines super-pixel segmentation and transmission optimization. First, the complexity of the haze image was calculated using color entropy to adaptively determine the number of super-pixel blocks. The simple linear iterative clustering (SLIC) super-pixel segmentation method was used to obtain super-pixel blocks with the same features. And the super-pixel block with the highest score was selected as a candidate block to accurately estimate the atmospheric light value. Then, the transmission was estimated using the multiscale dark channel prior and the non-local haze-lines prior, and then the initial transmission after fusion was obtained by wavelet transform. In addition, a guided filter based on unsharp masking was introduced to further improve the transmission estimation accuracy. Finally, an atmospheric scattering model was used to invert the haze-free image. We have conducted a large number of quantitative and qualitative experiments on three datasets, and the results show that the proposed algorithm can achieve a better de-fogging effect, especially in the sky region, where the image restoration effect is more prominent.