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Reliable Image Dehazing by NeRF

2023-03-16 08:34:03
Zheyan Jin, Shiqi Chen, Huajun Feng, Zhihai Xu, Qi Li, Yueting Chen

Abstract

We present an image dehazing algorithm with high quality, wide application, and no data training or prior needed. We analyze the defects of the original dehazing model, and propose a new and reliable dehazing reconstruction and dehazing model based on the combination of optical scattering model and computer graphics lighting rendering model. Based on the new haze model and the images obtained by the cameras, we can reconstruct the three-dimensional space, accurately calculate the objects and haze in the space, and use the transparency relationship of haze to perform accurate haze removal. To obtain a 3D simulation dataset we used the Unreal 5 computer graphics rendering engine. In order to obtain real shot data in different scenes, we used fog generators, array cameras, mobile phones, underwater cameras and drones to obtain haze data. We use formula derivation, simulation data set and real shot data set result experimental results to prove the feasibility of the new method. Compared with various other methods, we are far ahead in terms of calculation indicators (4 dB higher quality average scene), color remains more natural, and the algorithm is more robust in different scenarios and best in the subjective perception.

Abstract (translated)

我们提出了一种高质量的图像去雾算法,具有广泛的应用,不需要数据训练或先前的需要。我们对原始去雾模型的缺陷进行分析,并提出了基于光学散射模型和计算机图形照明渲染模型的新可靠去雾重建和去雾模型。基于新的雾模型和从摄像头获取的图像,我们可以重建三维空间,准确地计算空间中的物体和雾,并使用雾的透明度关系进行准确的雾去除。为了获得一个3D模拟数据集,我们使用了虚幻5计算机图形渲染引擎。为了在不同场景获取实际拍摄数据,我们使用了雾生成器、数组摄像头、智能手机、水下摄像头和无人机来获取雾数据。我们使用公式推导、模拟数据集和实际拍摄数据集的结果实验结果来证明新方法的可行性。与各种其他方法相比,我们在计算指标方面远远领先(平均场景质量提高4dB),颜色仍然更加自然,算法在不同场景下更加稳健,并且在主观感知方面表现最佳。

URL

https://arxiv.org/abs/2303.09153

PDF

https://arxiv.org/pdf/2303.09153.pdf


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