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.

中文翻译：

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基于近红外和可见光双通道传感器信息融合的除雾技术

由于从图像中去除雾气的方法比较复杂，并且雾化后的图像可能会出现细节损失和色彩失真，因此提出了一种基于近红外和可见光图像融合的除雾方法。该算法将细节丰富的近红外图像作为新的数据源，并采用图像融合的方法获得细节丰富，色彩恢复率高的除雾图像。首先，将彩色可见图像转换为HSI颜色空间，以获得强度通道图像，颜色通道图像和饱和度通道图像。将强度通道图像与近红外图像融合并去雾，然后通过非下采样Shearlet变换将其分解。通过使用双指数边缘平滑滤波器保留边缘，对获得的高频系数进行滤波，对低频系数进行低频抗锐化掩蔽处理。新的强度通道图像可以基于融合规则并通过倒数变换获得。然后，在对可见图像进行色彩处理时，建立饱和度图像的退化模型，该模型基于深色原色的原理估计参数，以获得估计的饱和度图像。最后，将新的强度通道图像，估计的饱和度图像和原色图像反射到RGB空间以获得融合图像，该融合图像通过颜色和清晰度校正得到增强。为了证明该算法的有效性，将浓雾图像和薄雾图像与流行的单图像除雾和多图像除雾算法以及基于深度学习的可见光-近红外融合除雾算法进行了比较。实验结果表明，与现有的高效除雾方法相比，该算法在改善图像的边缘对比度和视觉清晰度方面具有更好的效果。