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
Determination of an age in a particular tree species can be considered as a vital factor in forest management.In this research we have introduced a novel scheme to determine the accurate age of the tree species in Sri...Determination of an age in a particular tree species can be considered as a vital factor in forest management.In this research we have introduced a novel scheme to determine the accurate age of the tree species in Sri Lanka.This is initially developed for the tree species called‘Hora’(Dipterocarpus zeylanicus)in wet zone of Sri Lanka.Here the core samples are extracted and further analyzed by means of the different image processing techniques such as Gaussian kernel blurring,use of Sobel filters,double threshold analysis,Hough line tran sformation and etc.The operations such as rescaling,slicing and measuring are also used in line with image processing techniques to achieve the desired results.Ultimately a Graphical user interface(GUI)is developed to cater for the user requirements in a user friendly environment.It has been found that the average growth ring identification accuracy of the proposed system is 93%and the overall average accuracy of detecting the age is 81%.Ultimately the proposed system will provide an insight and contributes to the forestry related activities and researches in Sri Lanka.展开更多
Depth from defocus is one technology for depth estimation.We estimate particle depth information from two defocused images captured simultaneously by two coaxial cameras with different imaging distances.The images are...Depth from defocus is one technology for depth estimation.We estimate particle depth information from two defocused images captured simultaneously by two coaxial cameras with different imaging distances.The images are processed with the Fourier transform to obtain the characteristic parameter(i.e.,the standard deviation of the relative blur kernel of these two defocused images).First,we theoretically analyze the functional relationship between the object depth and the standard deviation or variation of the relative blur kernel.Then,we verify the relationship experimentally.We analyze the influence of particle size,window size and image noise on the calibration curves using both numerical simulations and experiments.We obtain the depth range and accuracy of this measurement system experimentally.For the verification experiments,we use a sample of glass microbeads and the irregularly-shaped dust particles on a microscope slide.Both of these experiments present a suitable depth measurement result.Finally,we apply the measuring system to the depth estimation of drops from a small anti-fogging spray.The results show that our system and image processing algorithm are robust for different types of particles,facilitating the in-line three-dimensional positioning of particles.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Determination of an age in a particular tree species can be considered as a vital factor in forest management.In this research we have introduced a novel scheme to determine the accurate age of the tree species in Sri Lanka.This is initially developed for the tree species called‘Hora’(Dipterocarpus zeylanicus)in wet zone of Sri Lanka.Here the core samples are extracted and further analyzed by means of the different image processing techniques such as Gaussian kernel blurring,use of Sobel filters,double threshold analysis,Hough line tran sformation and etc.The operations such as rescaling,slicing and measuring are also used in line with image processing techniques to achieve the desired results.Ultimately a Graphical user interface(GUI)is developed to cater for the user requirements in a user friendly environment.It has been found that the average growth ring identification accuracy of the proposed system is 93%and the overall average accuracy of detecting the age is 81%.Ultimately the proposed system will provide an insight and contributes to the forestry related activities and researches in Sri Lanka.
基金The authors gratefully acknowledge support from the National Natural Science Foundation of China(51576130,51327803)the Basic Research Program of Major Projects for Aeronautical and Gas Turbines(2017-V-0016-0069)the Educational Development Foundation of Shanghai Municipal Education Commission(14CG46).
文摘Depth from defocus is one technology for depth estimation.We estimate particle depth information from two defocused images captured simultaneously by two coaxial cameras with different imaging distances.The images are processed with the Fourier transform to obtain the characteristic parameter(i.e.,the standard deviation of the relative blur kernel of these two defocused images).First,we theoretically analyze the functional relationship between the object depth and the standard deviation or variation of the relative blur kernel.Then,we verify the relationship experimentally.We analyze the influence of particle size,window size and image noise on the calibration curves using both numerical simulations and experiments.We obtain the depth range and accuracy of this measurement system experimentally.For the verification experiments,we use a sample of glass microbeads and the irregularly-shaped dust particles on a microscope slide.Both of these experiments present a suitable depth measurement result.Finally,we apply the measuring system to the depth estimation of drops from a small anti-fogging spray.The results show that our system and image processing algorithm are robust for different types of particles,facilitating the in-line three-dimensional positioning of particles.
