Defogging Technology Based on Dual-Channel Sensor Information Fusion of Near-Infrared and Visible Light

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.

[1]  Gang Xiao,et al.  VIFB: A Visible and Infrared Image Fusion Benchmark , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[2]  Jean-Philippe Tarel,et al.  BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES , 2011 .

[3]  Codruta O. Ancuti,et al.  Single Image Dehazing by Multi-Scale Fusion , 2013, IEEE Transactions on Image Processing.

[4]  Bhabatosh Chanda,et al.  Learning a Patch Quality Comparator for Single Image Dehazing , 2018, IEEE Transactions on Image Processing.

[5]  Hui Li,et al.  Infrared and Visible Image Fusion with ResNet and zero-phase component analysis , 2018, Infrared Physics & Technology.

[6]  Ling Shao,et al.  Single Image Dehazing Based on the Physical Model and MSRCR Algorithm , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  V. Rajamani,et al.  Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images , 2015, Int. J. Imaging Syst. Technol..

[9]  Ling-Yu Duan,et al.  Multi-scale Optimal Fusion model for single image dehazing , 2019, Signal Process. Image Commun..

[10]  Shao Zhenfeng,et al.  Fusion of infrared and visible images based on focus measure operators in the curvelet domain. , 2012, Applied optics.

[11]  Sun Li,et al.  Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters , 2016, Inf. Fusion.

[12]  Xifang Zhu,et al.  Single image haze removal based on fusion darkness channel prior , 2017 .

[13]  Chang-Hwan Son,et al.  Near-Infrared Fusion via Color Regularization for Haze and Color Distortion Removals , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Dilbag Singh,et al.  Single image haze removal using integrated dark and bright channel prior , 2018 .

[15]  Yu Liu,et al.  Infrared and visible image fusion with convolutional neural networks , 2017, Int. J. Wavelets Multiresolution Inf. Process..

[16]  Hong Wang,et al.  Target-Aware Fusion of Infrared and Visible Images , 2018, IEEE Access.

[17]  Zhengguo Li,et al.  Single Image De-Hazing Using Globally Guided Image Filtering , 2018, IEEE Transactions on Image Processing.