Super-resolution reconstruction algorithm produces a high-resolution imagefrom a low-resolution image sequence. The accuracy and the stability of the motion estimation (ME)are essential for the whole restoration. In t...Super-resolution reconstruction algorithm produces a high-resolution imagefrom a low-resolution image sequence. The accuracy and the stability of the motion estimation (ME)are essential for the whole restoration. In this paper, a new super-resolution reconstructionalgorithm is developed using a robust ME method, which fuses multiple estimated motion vectorswithin the sequence. The new algorithm has two major improvements compared with the previousresearch. First, instead of only two frames, the whole sequence is used to obtain a more accurateand stable estimation of the motion vector of each frame; second, the reliability of the ME isquantitatively measured and introduced into the cost function of the reconstruction algorithm. Thealgorithm is applied to both synthetic and real sequences, and the results are presented in thepaper.展开更多
Motion blur restoration is essential for the imaging of moving objects,especially for single-pixel imaging(SPI),which requires multiple measurements.To reconstruct the image of a moving object with multiple motion mod...Motion blur restoration is essential for the imaging of moving objects,especially for single-pixel imaging(SPI),which requires multiple measurements.To reconstruct the image of a moving object with multiple motion modes,we propose a novel motion blur restoration method of SPI using geometric moment patterns.We design a novel localization method that uses normalized differential first-order moments and central moment patterns to determine the object's translational position and rotation angle information.Then,we perform motion compensation by using shifting Hadamard patterns.Our method effectively improves the detection accuracy of multiple motion modes and enhances the quality of the reconstructed image.We perform simulations and experiments,and the results validate the effectiveness of the proposed method.展开更多
文摘Super-resolution reconstruction algorithm produces a high-resolution imagefrom a low-resolution image sequence. The accuracy and the stability of the motion estimation (ME)are essential for the whole restoration. In this paper, a new super-resolution reconstructionalgorithm is developed using a robust ME method, which fuses multiple estimated motion vectorswithin the sequence. The new algorithm has two major improvements compared with the previousresearch. First, instead of only two frames, the whole sequence is used to obtain a more accurateand stable estimation of the motion vector of each frame; second, the reliability of the ME isquantitatively measured and introduced into the cost function of the reconstruction algorithm. Thealgorithm is applied to both synthetic and real sequences, and the results are presented in thepaper.
文摘Motion blur restoration is essential for the imaging of moving objects,especially for single-pixel imaging(SPI),which requires multiple measurements.To reconstruct the image of a moving object with multiple motion modes,we propose a novel motion blur restoration method of SPI using geometric moment patterns.We design a novel localization method that uses normalized differential first-order moments and central moment patterns to determine the object's translational position and rotation angle information.Then,we perform motion compensation by using shifting Hadamard patterns.Our method effectively improves the detection accuracy of multiple motion modes and enhances the quality of the reconstructed image.We perform simulations and experiments,and the results validate the effectiveness of the proposed method.
基金国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.2006AA012324)航空基金(the Aeronautical Science Foundation No.20060853010)高等院校博士学科点专项科研基金(the China Specialized Research Fundfor the Doctoral Program of Higher Education under Grant No.20040699034)
