Under dynamic conditions, the smearing effect of star spots on the image plane reduces centroid extraction accuracy, which has an impact on attitude estimation. To enhance the dynamic performance of the star sensor, w...Under dynamic conditions, the smearing effect of star spots on the image plane reduces centroid extraction accuracy, which has an impact on attitude estimation. To enhance the dynamic performance of the star sensor, we propose a multiplication extended Kalman filter (MEKF)-aided non-blind star image restoration algorithm based on the heterogeneous blur kernel. The proposed algorithm consists of three procedures. First, the MEKF is used to estimate the attitude and gyro drift to eliminate the measurement error of the star sensor and gyro drift. Second, the attitude predicted by MEKF is used, which provides initial conditions and accelerates the subsequent algorithm. Finally, a gyro-assisted heterogeneous blur kernel estimation algorithm is presented for restoring non-uniform and nonlinear motion-blurred star images. In contrast to existing dynamic star image deblurring algorithms, which focus mostly on image content, the proposed method emphasizes the cause of motion blur by fusing MEKF and a heterogeneous blur kernel. This leads to significantly enhanced robustness against noise and improved restoration accuracy. Simulation results demonstrate that the proposed method significantly outperforms existing techniques, improving centroid extraction accuracy by up to 59.64% and pointing accuracy across all axes by more than 78.94%.展开更多
Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and ...Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods.Due to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant progress.However,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred images.To this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated differentially.In particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right views.Based on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image regions.Moreover,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch.In this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions.Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)methods.For the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.展开更多
为了提高低分辨率模糊图像的质量,提出了一种基于自适应双l_p-l_2范数的超分辨率盲重建方法。该方法分为模糊核估计子过程和超分辨率非盲重建子过程。在模糊核估计子过程中,使用双l_p-l_2范数先验同时约束锐化图像和模糊核的估计,并使...为了提高低分辨率模糊图像的质量,提出了一种基于自适应双l_p-l_2范数的超分辨率盲重建方法。该方法分为模糊核估计子过程和超分辨率非盲重建子过程。在模糊核估计子过程中,使用双l_p-l_2范数先验同时约束锐化图像和模糊核的估计,并使用图像梯度的阈值分割,实现锐化图像l_p-l_2范数约束的自适应组合;在超分辨率非盲重建子过程中,结合估计到的模糊核,使用基于非局部中心化稀疏表示的超分辨率方法重建出最终的高分辨率图像。仿真实验中,与基于双l_0-l_2范数的方法相比,该算法重建结果的平均峰值信噪比(PSNR)提高了0.16 d B,平均结构相似度(SSIM)提高了0.004 5,平均差方和比降低了0.13。实验结果表明,所提方法能估计出较准确的模糊核,最终的重建图像中,振铃得到有效抑制,图像质量较好。展开更多
文摘Under dynamic conditions, the smearing effect of star spots on the image plane reduces centroid extraction accuracy, which has an impact on attitude estimation. To enhance the dynamic performance of the star sensor, we propose a multiplication extended Kalman filter (MEKF)-aided non-blind star image restoration algorithm based on the heterogeneous blur kernel. The proposed algorithm consists of three procedures. First, the MEKF is used to estimate the attitude and gyro drift to eliminate the measurement error of the star sensor and gyro drift. Second, the attitude predicted by MEKF is used, which provides initial conditions and accelerates the subsequent algorithm. Finally, a gyro-assisted heterogeneous blur kernel estimation algorithm is presented for restoring non-uniform and nonlinear motion-blurred star images. In contrast to existing dynamic star image deblurring algorithms, which focus mostly on image content, the proposed method emphasizes the cause of motion blur by fusing MEKF and a heterogeneous blur kernel. This leads to significantly enhanced robustness against noise and improved restoration accuracy. Simulation results demonstrate that the proposed method significantly outperforms existing techniques, improving centroid extraction accuracy by up to 59.64% and pointing accuracy across all axes by more than 78.94%.
基金supported by the National Natural Science Foundation of China (61971165, 61922027, 61773295)in part by the Fundamental Research Funds for the Central Universities (FRFCU5710050119)+1 种基金the Natural Science Foundation of Heilongjiang Province(YQ2020F004)the Chinese Association for Artificial Intelligence(CAAI)-Huawei Mind Spore Open Fund
文摘Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods.Due to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant progress.However,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred images.To this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated differentially.In particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right views.Based on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image regions.Moreover,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch.In this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions.Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)methods.For the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.
文摘为了提高低分辨率模糊图像的质量,提出了一种基于自适应双l_p-l_2范数的超分辨率盲重建方法。该方法分为模糊核估计子过程和超分辨率非盲重建子过程。在模糊核估计子过程中,使用双l_p-l_2范数先验同时约束锐化图像和模糊核的估计,并使用图像梯度的阈值分割,实现锐化图像l_p-l_2范数约束的自适应组合;在超分辨率非盲重建子过程中,结合估计到的模糊核,使用基于非局部中心化稀疏表示的超分辨率方法重建出最终的高分辨率图像。仿真实验中,与基于双l_0-l_2范数的方法相比,该算法重建结果的平均峰值信噪比(PSNR)提高了0.16 d B,平均结构相似度(SSIM)提高了0.004 5,平均差方和比降低了0.13。实验结果表明,所提方法能估计出较准确的模糊核,最终的重建图像中,振铃得到有效抑制,图像质量较好。
