Restoration of haze-free images using generative adversarial network

Weichao Yi; Ming Liu; Liquan Dong; Yuejin Zhao; Xiaohua Liu; Mei Hui

doi:10.1117/12.2541893

14 February 2020 Restoration of haze-free images using generative adversarial network

Weichao Yi, Ming Liu, Liquan Dong, Yuejin Zhao, Xiaohua Liu, Mei Hui

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 114321J (2020) https://doi.org/10.1117/12.2541893
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Haze is the result of the interaction between specific climate and human activities. When observing objects in hazy conditions, optical system will produce degradation problems such as color attenuation, image detail loss and contrast reduction. Image haze removal is a challenging and ill-conditioned problem because of the ambiguities of unknown radiance and medium transmission. In order to get clean images, traditional machine vision methods usually use various constraints/prior conditions to obtain a reasonable haze removal solutions, the key to achieve haze removal is to estimate the medium transmission of the input hazy image in earlier studies. In this paper, however, we concentrated on recovering a clear image from a hazy input directly by using Generative Adversarial Network (GAN) without estimating the transmission matrix and atmospheric scattering model parameters, we present an end-to-end model that consists of an encoder and a decoder, the encoder is extracting the features of the hazy images, and represents these features in high dimensional space, while the decoder is employed to recover the corresponding images from high-level coding features. And based perceptual losses optimization could get high quality of textural information of haze recovery and reproduce more natural haze-removal images. Experimental results on hazy image datasets input shows better subjective visual quality than traditional methods. Furthermore, we test the haze removal images on a specialized object detection network- YOLO, the detection result shows that our method can improve the object detection performance on haze removal images, indicated that we can get clean haze-free images from hazy input through our GAN model.

Citation Download Citation

Weichao Yi, Ming Liu, Liquan Dong, Yuejin Zhao, Xiaohua Liu, and Mei Hui "Restoration of haze-free images using generative adversarial network", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114321J (14 February 2020); https://doi.org/10.1117/12.2541893

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KEYWORDS

Air contamination

Atmospheric modeling

Computer programming

Visual process modeling

Convolution

Gallium nitride

Performance modeling

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