Motion-blurred image deblurring of fast moving target
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摘要: 对单一图像进行运动模糊复原，存在模糊点扩散函数（PSF）难以估计以及图像反卷积的病态性问题。利用多个PSF具有联合可逆性的特点，针对运动目标观测，提出采用参数相同的多个成像设备共同对同一视场进行拍摄，来获取背景相同、曝光时间不同、目标模糊程度不同的观测图像；然后利用同一设备获取的序列图像进行目标的模糊PSF估计；并根据目标背景的运动模糊叠加特征，分别从观测图像中提取出完整的模糊目标图像；最后，对这些具有不同PSF的同一目标图像进行空间域迭代复原算式的联立求解。实验表明：该方法设计的目标获取装置对硬件条件要求较低，获取的图像更便于采用多点扩散函数联合进行图像复原，复原效果良好。Abstract: In the process of a single motion-blurred image restoration, it is difficult to estimate point spread function(PSF) and the ill-posed deconvolution will affect restoration result. Considering that several different PSFs can get joint invertibility to make deblurring well-posed, it was proposed that the motion-blurred images of different PSFs could be obtained by multiple cameras with same parameters to shoot continuously at high speed moving target in one field of view. The images had the same brightness, different exposure time, and different motion blur. The PSFs were easy to be estimated by consecutive frames. According to the motion-blurred character of the target and background imaging pixels, complete convolution blurred images of the target could be extracted from the observation images respectively. Finally, the target image was recovered by iterative restoration in the spatial domain by multiple images joint solution. Experiments show that the method only needs common hardware, and the obtained images can help more exact image restoration by joint PSFs solution. And the restoration results maintain details well and have lower signal noise ratio (SNR).
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