AIDEDNet: anti-interference and detail enhancement dehazing network for real-world scenes

Abstract

The haze phenomenon seriously interferes the image acquisition and reduces image quality. Due to many uncertain factors, dehazing is typically a challenge in image processing. The most existing deep learning-based dehazing approaches apply the atmospheric scattering model (ASM) or a similar physical model, which originally comes from traditional dehazing methods. However, the data set trained in deep learning does not match well this model for three reasons. Firstly, the atmospheric illumination in ASM is obtained from prior experience, which is not accurate for dehazing real-scene. Secondly, it is difficult to get the depth of outdoor scenes for ASM. Thirdly, the haze is a complex natural phenomenon, and it is difficult to find an accurate physical model and related parameters to describe this phenomenon. In this paper, we propose a black box method, in which the haze is considered an image quality problem without using any physical model such as ASM. Analytically, we propose a novel dehazing equation to combine two mechanisms: interference item and detail enhancement item. The interference item estimates the haze information for dehazing the image, and then the detail enhancement item can repair and enhance the details of the dehazed image. Based on the new equation, we design an anti-interference and detail enhancement dehazing network (AIDEDNet), which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training. Specifically, we propose a new way to construct a haze patch on the flight of network training. The patch is randomly selected from the input images and the thickness of haze is also randomly set. Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.

Graphical abstract

Keywords

dehaze / anti-interference / detail enhancement / network

Jian Zhang is currently a PhD candidate at the School of Computer Science in Wuhan University, China. His research interests include image processing, pattern recognition, and computer graphics

Fazhi He is currently a professor at the School of Computer Science of Wuhan University, China. His research interests include collaborative computing, computer graphics, computer vision, and high-performance computing

Yansong Duan received the MS, and PhD degrees from Wuhan University, China in 2009 and 2016, respectively. He is currently an associate professor with the School of Remote Sensing and Information Engineering, Wuhan University, China. His research interests include photogrammetry, image processing and matching, 3-D city reconstruction, computer vision, and high performance computing

Shizhen Yang received his BE degree in Computer Science and Technology from Wuhan University, China in 2019. Currently, he is pursuing a ME degree in Computer Science and Technology at Wuhan University, China. His research interest includes image restoration

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62072348), the National Key RD Program of China under (2019YFC1509604), and the Science and Technology Major Project of Hubei Province China (Next-Generation AI Technologies) (2019AEA170)

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