An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks
Unmanned aerial vehicles (UAVs) are an inexpensive platform for collecting remote sensing images, but UAV images suffer from a content loss problem caused by noise. In order to solve the noise problem of UAV images, we propose a new methods to denoise UAV images. This paper introduces a novel deep n...
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doaj-9343555abd164f37a64b33586c08af182020-11-25T02:31:01ZengMDPI AGSensors1424-82202018-06-01187198510.3390/s18071985s18071985An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial NetworksRuihua Wang0Xiongwu Xiao1Bingxuan Guo2Qianqing Qin3Ruizhi Chen4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaUnmanned aerial vehicles (UAVs) are an inexpensive platform for collecting remote sensing images, but UAV images suffer from a content loss problem caused by noise. In order to solve the noise problem of UAV images, we propose a new methods to denoise UAV images. This paper introduces a novel deep neural network method based on generative adversarial learning to trace the mapping relationship between noisy and clean images. In our approach, perceptual reconstruction loss is used to establish a loss equation that continuously optimizes a min-max game theoretic model to obtain better UAV image denoising results. The generated denoised images by the proposed method enjoy clearer ground objects edges and more detailed textures of ground objects. In addition to the traditional comparison method, denoised UAV images and corresponding original clean UAV images were employed to perform image matching based on local features. At the same time, the classification experiment on the denoised images was also conducted to compare the denoising results of UAV images with others. The proposed method had achieved better results in these comparison experiments.http://www.mdpi.com/1424-8220/18/7/1985UAV imagesimage denoisinggenerative adversarial networksperceptual reconstruction loss |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ruihua Wang Xiongwu Xiao Bingxuan Guo Qianqing Qin Ruizhi Chen |
spellingShingle |
Ruihua Wang Xiongwu Xiao Bingxuan Guo Qianqing Qin Ruizhi Chen An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks Sensors UAV images image denoising generative adversarial networks perceptual reconstruction loss |
author_facet |
Ruihua Wang Xiongwu Xiao Bingxuan Guo Qianqing Qin Ruizhi Chen |
author_sort |
Ruihua Wang |
title |
An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks |
title_short |
An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks |
title_full |
An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks |
title_fullStr |
An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks |
title_full_unstemmed |
An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks |
title_sort |
effective image denoising method for uav images via improved generative adversarial networks |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-06-01 |
description |
Unmanned aerial vehicles (UAVs) are an inexpensive platform for collecting remote sensing images, but UAV images suffer from a content loss problem caused by noise. In order to solve the noise problem of UAV images, we propose a new methods to denoise UAV images. This paper introduces a novel deep neural network method based on generative adversarial learning to trace the mapping relationship between noisy and clean images. In our approach, perceptual reconstruction loss is used to establish a loss equation that continuously optimizes a min-max game theoretic model to obtain better UAV image denoising results. The generated denoised images by the proposed method enjoy clearer ground objects edges and more detailed textures of ground objects. In addition to the traditional comparison method, denoised UAV images and corresponding original clean UAV images were employed to perform image matching based on local features. At the same time, the classification experiment on the denoised images was also conducted to compare the denoising results of UAV images with others. The proposed method had achieved better results in these comparison experiments. |
topic |
UAV images image denoising generative adversarial networks perceptual reconstruction loss |
url |
http://www.mdpi.com/1424-8220/18/7/1985 |
work_keys_str_mv |
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