Abstract

Abstract The misty, foggy, or hazy weather conditions lead to image color distortion and reduce the resolution and the contrast of the observed object in outdoor scene acquisition. In order to detect and remove haze, this article proposes a novel effective algorithm for visibility enhancement from a single gray or color image. Since it can be considered that the haze mainly concentrates in one component of the multilayer image, the haze-free image is reconstructed through haze layer estimation based on the image filtering approach using both low-rank technique and the overlap averaging scheme. By using parallel analysis with Monte Carlo simulation from the coarse atmospheric veil by the median filter, the refined smooth haze layer is acquired with both less texture and retaining depth changes. With the dark channel prior, the normalized transmission coefficient is calculated to restore fogless image. Experimental results show that the proposed algorithm is a simpler and efficient method for clarity improvement and contrast enhancement from a single foggy image. Moreover, it can be comparable with the state-of-the-art methods, and even has better results than them.

Highlights

  • Visibility is the ability to see through air, irrelevant to the sunlight or the moonlight

  • There are a number of factors affecting visibility, including precipitation, fog, mist, haze, smoke, and in coastal areas sea spray, and they are generally composed principally of water droplets or the particles whose size cannot be ignored for the wavelength

  • Compared with the state-of-the-art methods, the proposed algorithm based on image filtering approach has a little longer computation time than that of [21], and much shorter calculation time than that of [20]

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Summary

Introduction

Visibility is the ability to see through air, irrelevant to the sunlight or the moonlight. Since the atmospheric veil almost has no specific edges or textures, we may adopt image filtering approach to estimate the haze layer from the hazy image.

Results
Conclusion

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