RT-Deblur: real-time image deblurring for object detection

Wang, Hanzhao; Hu, Chunhua; Qian, Weijie; Q, Wang

doi:10.1007/s00371-023-02991-y

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Despite their potential, these methods are constrained by the accuracy of blurring kernel estimation and exhibit low execution efficiency [19]. While effective for specific scene models, they lack robust generalization capabilities for addressing more challenging real-world scene deblurring tasks [20].…”

Section: Introductionmentioning

confidence: 99%

Forest Fire Image Deblurring Based on Spatial–Frequency Domain Fusion

Kong,

Liu,

Han

et al. 2024

Forests

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UAVs are commonly used in forest fire detection, but the captured fire images often suffer from blurring due to the rapid motion between the airborne camera and the fire target. In this study, a multi-input, multi-output U-Net architecture that combines spatial domain and frequency domain information is proposed for image deblurring. The architecture includes a multi-branch dilated convolution attention residual module in the encoder to enhance receptive fields and address local features and texture detail limitations. A feature-fusion module integrating spatial frequency domains is also included in the skip connection structure to reduce feature loss and enhance deblurring performance. Additionally, a multi-channel convolution attention residual module in the decoders improves the reconstruction of local and contextual information. A weighted loss function is utilized to enhance network stability and generalization. Experimental results demonstrate that the proposed model outperforms popular models in terms of subjective perception and quantitative evaluation, achieving a PSNR of 32.26 dB, SSIM of 0.955, LGF of 10.93, and SMD of 34.31 on the self-built forest fire datasets and reaching 86% of the optimal PSNR and 87% of the optimal SSIM. In experiments without reference images, the model performs well in terms of LGF and SMD. The results obtained by this model are superior to the currently popular SRN and MPRNet models.

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Section: Introductionmentioning

confidence: 99%

Forest Fire Image Deblurring Based on Spatial–Frequency Domain Fusion

Kong,

Liu,

Han

et al. 2024

Forests

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“…Typically, to employ GAN for image deblurring, a dataset consisting of paired sharp and blurred images is required [26]. However, in the case of images captured by UAVs, reference images are often unavailable, necessitating the artificial synthesis of blurred images by combining consecutive frames [27]. Nevertheless, this process may introduce discrepancies between artificially synthesized blur characteristics and those occurring naturally.…”

Section: Introductionmentioning

confidence: 99%

A Motion Deblurring Network for Enhancing UAV Image Quality in Bridge Inspection

Lee,

Gwon,

Kim

et al. 2023

Drones

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Unmanned aerial vehicles (UAVs) have been increasingly utilized for facility safety inspections due to their superior safety, cost effectiveness, and inspection accuracy compared to traditional manpower-based methods. High-resolution images captured by UAVs directly contribute to identifying and quantifying structural defects on facility exteriors, making image quality a critical factor in achieving accurate results. However, motion blur induced by external factors such as vibration, low light conditions, and wind during UAV operation significantly degrades image quality, leading to inaccurate defect detection and quantification. To address this issue, this research proposes a deblurring network using a Generative Adversarial Network (GAN) to eliminate the motion blur effect in UAV images. The GAN-based motion deblur network represents an image inpainting method that leverages generative models to correct blurry artifacts, thereby generating clear images. Unlike previous studies, this proposed approach incorporates deblur and blur learning modules to realistically generate blur images required for training the generative models. The UAV images processed using the motion deblur network are evaluated using a quality assessment method based on local blur map and other well-known image quality assessment (IQA) metrics. Moreover, in the experiment of crack detection utilizing the object detection system, improved detection results are observed when using enhanced images. Overall, this research contributes to improving the quality and accuracy of facility safety inspections conducted with UAV-based inspections by effectively addressing the challenges associated with motion blur effects in UAV-captured images.

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Robust and fast QR code images deblurring via local maximum and minimum intensity prior

Xu,

Jin,

et al. 2024

Vis Comput

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RT-Deblur: real-time image deblurring for object detection

Cited by 4 publications

References 38 publications

Forest Fire Image Deblurring Based on Spatial–Frequency Domain Fusion

Forest Fire Image Deblurring Based on Spatial–Frequency Domain Fusion

A Motion Deblurring Network for Enhancing UAV Image Quality in Bridge Inspection

Robust and fast QR code images deblurring via local maximum and minimum intensity prior

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