Abstract
Since the method to remove fog from images is complicated and detail loss and color distortion could occur to the defogged images, a defogging method based on near-infrared and visible image fusion is put forward in this paper. The algorithm in this paper uses the near-infrared image with rich details as a new data source and adopts the image fusion method to obtain a defog image with rich details and high color recovery. First, the colorful visible image is converted into HSI color space to obtain an intensity channel image, color channel image, and saturation channel image. The intensity channel image is fused with a near-infrared image and defogged, and then it is decomposed by Nonsubsampled Shearlet Transform. The obtained high-frequency coefficient is filtered by preserving the edge with a double exponential edge smoothing filter, while low-frequency antisharpening masking treatment is conducted on the low-frequency coefficient. The new intensity channel image could be obtained based on the fusion rule and by reciprocal transformation. Then, in color treatment of the visible image, the degradation model of the saturation image is established, which estimates the parameters based on the principle of dark primary color to obtain the estimated saturation image. Finally, the new intensity channel image, the estimated saturation image, and the primary color image are reflected to RGB space to obtain the fusion image, which is enhanced by color and sharpness correction. In order to prove the effectiveness of the algorithm, the dense fog image and the thin fog image are compared with the popular single image defogging and multiple image defogging algorithms and the visible light-near infrared fusion defogging algorithm based on deep learning. The experimental results show that the proposed algorithm is better in improving the edge contrast and the visual sharpness of the image than the existing high-efficiency defogging method.
Highlights
Fog is an atmospheric phenomenon because the light is absorbed or reflected by the floating particles composed of water vapor, suspended matters, and aerosol in the air
(5) We propose a color correction method, color and sharpness correction (CSC) color correction is conducted on the fused image, which could improve detail lack caused by uneven illumination due to fog, making the image color more natural and the detail features of the shadow region enhanced
In order to improve the utilization of the dataset and avoid overfitting of the deep learning network, the cropped image is enhanced by rotation, and the data is enhanced to 6 times of the original data set, reaching 46,080 groups
Summary
Fog is an atmospheric phenomenon because the light is absorbed or reflected by the floating particles composed of water vapor, suspended matters, and aerosol in the air Under such wicked condition, due to atmospheric scattering, the scenic radiation obtained would be weakened or polluted sharply, resulting in the captured images blurred, for example, the image contrast, content details, and color saturation would deteriorate gradually with the increase of the scenic distance and the hazy level. Enhancement-based defogging algorithms improve visual effect mainly by improving the image contrast, but neglecting the nature degradation of the image quality. Such methods include mainly the histogram equalization method
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