Abstract
Single-image dehazing techniques are extensively used in outdoor optical image acquisition equipment. Most existing methods pay attention to use various priors to estimate scene transmission. In this paper, a fast single-image dehazing algorithm is proposed based on a piecewise transformation model between the minimum channels of the hazy image and the haze-free image in optical model. The minimum channel of the haze-free image is obtained by the piecewise transformation, which is a quadratic function model that we establish for the dark region, and a linear transformation model is established for the bright region. Using the minimum channels of the hazy image and the haze-free image, a transmission estimation model is established based on the haze optical model with adjustment variables. To obtain an accurate estimation of atmospheric light, we estimate the atmospheric light twice. Finally, the haze-free image is restored. Experimental results show that the proposed algorithm has minimal halo artifacts and color distortion in various depths of field, flat areas and sky areas. From the subjective evaluation, objective evaluation and running time analysis, it can be seen that the algorithm in this paper is superior to most existing technologies.
Highlights
Images captured in hazy scenes often suffer from visibility reduction and color degradation due to atmospheric scattering caused by atmospheric particles [1]–[3]
The minimum channel of the haze-free image is obtained by the piecewise transformation, which is a quadratic function model that we establish for the dark region, and a linear transformation model is established for the bright region
For a single hazy image, a fast dehazing method based on a piecewise transformation model is proposd
Summary
Images captured in hazy scenes often suffer from visibility reduction and color degradation due to atmospheric scattering caused by atmospheric particles [1]–[3]. To overcome incorrect transmission estimation caused by a depth error in the skyline or wrong haze information, Shin et al [34] presented a novel optimization-based dehazing algorithm that combined the radiance and reflectance components of an image with an additional refinement using a structure-guided l0-norm filter. Li et al [44] proposed an encoder and decoder architecture which different from the generative network in basic conditional GANs, where the clear image is estimated by an end-to-end trainable neural network These methods are limited in some applications where additional information or multiple images are not available, and single-image dehazing based on the haze-optical model has no such limitation.
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