Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data
Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/2/96 |
| _version_ | 1797621424877207552 |
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| author | Baoyu Zhu Qunbo Lv Zheng Tan |
| author_facet | Baoyu Zhu Qunbo Lv Zheng Tan |
| author_sort | Baoyu Zhu |
| collection | DOAJ |
| description | Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of blurred aerial remote sensing images. Moreover, the average PSNR of this paper’s algorithm tested on the publicly available dataset RealBlur-R reached 41.02 dB, surpassing the latest SOTA algorithm. |
| first_indexed | 2024-03-11T08:55:45Z |
| format | Article |
| id | doaj.art-39690b02d28b4e47b455c9ba13e22fff |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T08:55:45Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-39690b02d28b4e47b455c9ba13e22fff2023-11-16T20:06:32ZengMDPI AGDrones2504-446X2023-01-01729610.3390/drones7020096Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image DataBaoyu Zhu0Qunbo Lv1Zheng Tan2Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaDrone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of blurred aerial remote sensing images. Moreover, the average PSNR of this paper’s algorithm tested on the publicly available dataset RealBlur-R reached 41.02 dB, surpassing the latest SOTA algorithm.https://www.mdpi.com/2504-446X/7/2/96drone and aerial remote sensingimage deblurringgenerative adversarial networksmulti-scaleimage blur levelobject detection |
| spellingShingle | Baoyu Zhu Qunbo Lv Zheng Tan Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data Drones drone and aerial remote sensing image deblurring generative adversarial networks multi-scale image blur level object detection |
| title | Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data |
| title_full | Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data |
| title_fullStr | Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data |
| title_full_unstemmed | Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data |
| title_short | Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data |
| title_sort | adaptive multi scale fusion blind deblurred generative adversarial network method for sharpening image data |
| topic | drone and aerial remote sensing image deblurring generative adversarial networks multi-scale image blur level object detection |
| url | https://www.mdpi.com/2504-446X/7/2/96 |
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