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

Author:

Yuan Yubin1ORCID,Shen Yu12ORCID,Peng Jing1,Wang Lin1,Zhang Hongguo1

Affiliation:

1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

2. Key Laboratory of Opto-Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, China

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.

Funder

Ministry of Education of the People's Republic of China

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Instrumentation,Control and Systems Engineering

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. FPGA-Compatible Fog Rectification and Contrast Enhancement for Improved Vision Algorithms in Foggy Environments;2023 4th International Conference on Intelligent Technologies (CONIT);2024-06-21

2. Visible and Infrared Image Fusion Using Deep Learning;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023-08

3. Research on Image Defogging Enhancement Technology Based on Retinex Algorithm;2023 2nd International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME);2023-06

4. Night Vision Anti-Halation Algorithm of Different-Source Image Fusion Based on Low-Frequency Sequence Generation;Mathematics;2023-05-10

5. High-Efficiency Integrated Color Routers by Simple Identical Nanostructures for Visible and Near-Infrared Wavelengths;Photonics;2023-05-06

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