A key question of the backward integration algorithm to lidar equation is how to determine the far-endboundary value. This paper develops a Constraint Inversion Algorithm (CIA) for deriving the value andthen the aeros...A key question of the backward integration algorithm to lidar equation is how to determine the far-endboundary value. This paper develops a Constraint Inversion Algorithm (CIA) for deriving the value andthen the aerosol extinction profile from lidar signals, which uses the ground-level horizontal lidar signals asthe constraint information. The smaller the wavelength is, the more sensitive to the variation of aerosol extinction to backscatter ratio solved by CIA. According to the property an algorithm is further proposed tosimultaneously retrieve the aerosol extinction profile, the size distribution and the imaginary part of its reflective index from the multi-wavelength lidar observations. CIA is tested in the inversion simulations withsatisfactory result.展开更多
Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence ...Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography(OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image.Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.展开更多
文摘A key question of the backward integration algorithm to lidar equation is how to determine the far-endboundary value. This paper develops a Constraint Inversion Algorithm (CIA) for deriving the value andthen the aerosol extinction profile from lidar signals, which uses the ground-level horizontal lidar signals asthe constraint information. The smaller the wavelength is, the more sensitive to the variation of aerosol extinction to backscatter ratio solved by CIA. According to the property an algorithm is further proposed tosimultaneously retrieve the aerosol extinction profile, the size distribution and the imaginary part of its reflective index from the multi-wavelength lidar observations. CIA is tested in the inversion simulations withsatisfactory result.
基金Supported by National Key Research and Development Program of China(2016YFF0201005)。
文摘Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography(OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image.Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.
