Single Image Deblurring for Pulsed Laser Range-Gated Imaging System with Multi-Slice Integration

Lin, Hongsheng; Ma, Liheng; Hu, Qingping; Zhang, Mengjie; Xiong, Zhang; Han, Hainian

doi:10.3390/photonics9090642

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…It has been demonstrated that different levels of blur in images can be better handled from multi-scale images [28]. Lin et al [29] used a transformer-based deep learning model for image deblurring and used the imaging model to generate 16 different blurring kernels to adapt the model to different degrees of blurred images, and the results show that the proposed method can remove different degrees of blurring and can handle images with both clear and blurred targets. Based on this, various CNN-based deblurring methods have also adopted this idea where blurred images of different scales are used as inputs to each sub-network [30], and a coarse-to-fine design principle has proven to be effective for image deblurring.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Cyclic Image Deblurring Based on PVC-Resnet

et al. 2023

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Aiming at the non-uniform blurring of image caused by optical system defects or external interference factors, such as camera shake, out-of-focus, and fast movement of object, a multi-scale cyclic image deblurring model based on a parallel void convolution-Resnet (PVC-Resnet) is proposed in this paper, in which a multi-scale recurrent network architecture and a coarse-to-fine strategy are used to restore blurred images. The backbone network is built based on Unet codec architecture, where a PVC-Resnet module designed by combinations of parallel dilated convolution and residual network is constructed in the encoder of the backbone network. The convolution receptive field is expanded with parallel dilated convolution to extract richer global features. Besides, a multi-scale feature extraction module is designed to extract the shallow features of different scale targets in blurred images, and then the extracted features are sent to the backbone network for feature refinement. The SSIM loss function and the L1 loss function are combined to construct the SSIM-L1 joint loss function for the optimization of the overall network to ensure that the image restoration at different stages can be optimized. The experimental results show that the average peak signal-to-noise ratio (PSNR) of the proposed model on different data sets is as high as 32.84 dB, and the structural similarity (SSIM) reaches 0.9235. and statistical structural similarity (Stat-SSIM) of 0.9249 on different datasets. Compared with other methods, the deblurred images generated by this method are superior to the methods proposed by Nah et al., Kupyn et al. and Cho S J et al., especially on the calibration board data set. The model proposed in this paper applies parallel dilated convolution and SSIM-L1 joint loss function to improve the performance of the network so that the edge and texture details of the restored image are clearer.

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Section: Introductionmentioning

confidence: 99%

Multi-Scale Cyclic Image Deblurring Based on PVC-Resnet

et al. 2023

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Backlight Imaging Based on Laser-Gated Technology

Bai,

Zhang,

Gao

et al. 2024

Photonics

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Backlight imaging refers to the process of capturing images when the light source directly enters the lens of imaging devices or against a high-brightness background, which usually suffers from degraded imaging quality caused by direct or reflected strong light. Traditional backlight imaging methods involve reducing light flux, expanding dynamic range, and utilizing avoidance angles. However, these methods only partially address the issue of backlighting, and are unable to effectively extract information from the areas overwhelmed by the backlight. To overcome these limitations, this paper reported a backlight imaging technique based on active illumination laser gated imaging technology (AILGIT), originally applied in underwater scattering imaging. Given that backlight imaging is essentially a form of scattering imaging, this technique is likely applicable to backlight scenarios. The AILGIT employs nanosecond-gated imaging components synchronized with nanosecond pulse laser illumination to spatially slice the target. This method allows the camera to capture target signals within specific slices only, which effectively suppresses ambient light and scattering interference from the medium and achieves high-contrast imaging with strong backlight suppression. Experiments obtained dynamic backlight imaging results for a vehicle with headlight on at night from a distance of 500 m, with 60 frames per second and a 4.2 by 2.8 meters’ field of view, where wheel contours and the license plate can be clearly distinguished. The result not only demonstrates the potential of AILGIT in suppressing strong backlight, but also lays the foundation for further research on laser 3D imaging and subsequent processing techniques for backlight targets.

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Single Image Deblurring for Pulsed Laser Range-Gated Imaging System with Multi-Slice Integration

Cited by 2 publications

References 31 publications

Multi-Scale Cyclic Image Deblurring Based on PVC-Resnet

Multi-Scale Cyclic Image Deblurring Based on PVC-Resnet

Backlight Imaging Based on Laser-Gated Technology

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