Abstract

Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In this article, a method of image restoration for both haze and underwater scattering environment is proposed. It bases on the general assumption that gray variance and average gradient of a clear image are larger than those of an image in a scattering medium. Firstly, based on the assumption, polarimetric images with the maximum variance (Ibest) and minimum variance (Iworst) are calculated from the captured four polarization images. Secondly, the transmittance is estimated and used to remove the scattering light from background medium of Ibest and Iworst. Thirdly, two images are fused to form a clear image and the color is also restored. Experimental results show that the proposed method obtains clear restored images both in haze and underwater scattering media. Because it does not rely on the polarization degree or polarization angle, it is more universal and suitable for scenes with little polarized light.

Highlights

  • Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light

  • Zhu et al.[15,16] proposed a novel fast single image dehazing algorithm based on artificial multiexposure image fusion and an image dehazing method by an artificial image fusion method based on adaptive structure decomposition

  • Benefit from more information obtained from multiple different polarization images, restoration method based on polarization imaging has more advantages and better details than single image dehazing methods

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Summary

Introduction

Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In 2001, Schechner et al.[8,9,10] proposed an image defogging method based on polarization difference. Shao et al.[31] proposed a hazy image restoration method based on atmospheric light polarization tomography.

Results
Conclusion

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