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Sensors 2018, 18(8), 2606;

A Sensor Image Dehazing Algorithm Based on Feature Learning

College of Aeronautics Engineering, Air Force Engineering University, Xi’an 710038, China
Authors to whom correspondence should be addressed.
Received: 18 July 2018 / Revised: 6 August 2018 / Accepted: 6 August 2018 / Published: 9 August 2018
(This article belongs to the Section Intelligent Sensors)
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To solve the problems of color distortion and structure blurring in images acquired by sensors during bad weather, an image dehazing algorithm based on feature learning is put forward to improve the quality of sensor images. First, we extracted the multiscale structure features of the haze images by sparse coding and the various haze-related color features simultaneously. Then, the generative adversarial network (GAN) was used for sample training to explore the mapping relationship between different features and the scene transmission. Finally, the final haze-free image was obtained according to the degradation model. Experimental results show that the method has obvious advantages in its detail recovery and color retention. In addition, it effectively improves the quality of sensor images. View Full-Text
Keywords: image dehazing; feature learning; sparse coding; generative adversarial networks image dehazing; feature learning; sparse coding; generative adversarial networks

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Liu, K.; He, L.; Ma, S.; Gao, S.; Bi, D. A Sensor Image Dehazing Algorithm Based on Feature Learning. Sensors 2018, 18, 2606.

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