In various imaging applications such as autonomous vehicles and drones,autofocus lenses are indispensable for capturing clear images.However,conventional camera calibration methods typically rely either on processing ...In various imaging applications such as autonomous vehicles and drones,autofocus lenses are indispensable for capturing clear images.However,conventional camera calibration methods typically rely either on processing multiple images at a fixed focal length or on detecting multi-plane markers in a single image and then applying multi-image calibration models.This paper proposes a flexible and accurate calibration approach that extracts subpixel saddle points from a single image containing three non-coplanar calibration boards.To compute accurate homography matrices for the three boards,outliers are removed by eliminating chessboard points that deviated from the fitted grid lines according to their row and column positions.Initial estimates of the intrinsic parameters and the poses of the three planar chessboards are obtained using the three homography matrices in combination with Zhang’s calibration method.During parameter refinement,a multi-objective optimization function is constructed,incorporating three error terms:(1)Reprojection error of the inlier grid points;(2)Mechanism-driven error derived from the relationship between homography matrices and camera parameters;(3)Cross-planar linearity constraint error,which preserves the pre-imaging collinearity of any five points across different planes after projection.For weight selection in the optimization process,confidence intervals of the detected grid points are analyzed by horizontally rotating the reprojection lines to reduce bias introduced by line slope.The optimal weights are determined by minimizing the number of points whose confidence intervals does not intersect the reprojected lines.When multiple candidates yield similar reprojection performance,the parameter set with the smallest reprojection error is selected as the final result.This method efficiently estimates both intrinsic and extrinsic camera parameters.Simulations and real-world experiments validate the high precision and effectiveness of the proposed approach.Our technique is straightforward,practical,and holds significant theoretical and practical value for rapid and reliable camera calibration.展开更多
Rain streaks in an image appear in different sizes and orientations,resulting in severe blurring and visual quality degradation.Previous CNNbased algorithms have achieved encouraging deraining results although there a...Rain streaks in an image appear in different sizes and orientations,resulting in severe blurring and visual quality degradation.Previous CNNbased algorithms have achieved encouraging deraining results although there are certain limitations in the description of rain streaks and the restoration of scene structures in different environments.In this paper,we propose an efficient multi-scale enhancement and aggregation network(MEAN)to solve the single-image deraining problem.Considering the importance of large receptive fields and multi-scale features,we introduce a multi-scale enhanced unit(MEU)to capture longrange dependencies and exploit features at different scales to depict rain.Simultaneously,an attentive aggregation unit(AAU)is designed to utilize the informative features in spatial and channel dimensions,thereby aggregating effective information to eliminate redundant features for rich scenario details.To improve the deraining performance of the encoder–decoder network,we utilized an AAU to filter the information in the encoder network and concatenated the useful features to the decoder network,which is conducive to predicting high-quality clean images.Experimental results on synthetic datasets and real-world samples show that the proposed method achieves a significant deraining performance compared to state-of-the-art approaches.展开更多
Super-resolution(SR)is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the decades.The main concept of SR is to reconstruct images from low-resol...Super-resolution(SR)is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the decades.The main concept of SR is to reconstruct images from low-resolution(LR)to high-resolution(HR).It is an ongoing process in image technology,through up-sampling,de-blurring,and de-noising.Convolution neural network(CNN)has been widely used to enhance the resolution of images in recent years.Several alternative methods use deep learning to improve the progress of image super-resolution based on CNN.Here,we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution.,it is also to highlight the potential applications of image super-resolution in security monitoring,medical diagnosis,microscopy image processing,satellite remote sensing,communication transmission,the digital multimedia industry and video enhancement.Finally,we present the challenges and assess future trends in super-resolution based on deep learning.展开更多
基金supported by the Research on the Reform of Curriculum Assessment Methods for College Mathematics Platform Courses(No.53111104016)。
文摘In various imaging applications such as autonomous vehicles and drones,autofocus lenses are indispensable for capturing clear images.However,conventional camera calibration methods typically rely either on processing multiple images at a fixed focal length or on detecting multi-plane markers in a single image and then applying multi-image calibration models.This paper proposes a flexible and accurate calibration approach that extracts subpixel saddle points from a single image containing three non-coplanar calibration boards.To compute accurate homography matrices for the three boards,outliers are removed by eliminating chessboard points that deviated from the fitted grid lines according to their row and column positions.Initial estimates of the intrinsic parameters and the poses of the three planar chessboards are obtained using the three homography matrices in combination with Zhang’s calibration method.During parameter refinement,a multi-objective optimization function is constructed,incorporating three error terms:(1)Reprojection error of the inlier grid points;(2)Mechanism-driven error derived from the relationship between homography matrices and camera parameters;(3)Cross-planar linearity constraint error,which preserves the pre-imaging collinearity of any five points across different planes after projection.For weight selection in the optimization process,confidence intervals of the detected grid points are analyzed by horizontally rotating the reprojection lines to reduce bias introduced by line slope.The optimal weights are determined by minimizing the number of points whose confidence intervals does not intersect the reprojected lines.When multiple candidates yield similar reprojection performance,the parameter set with the smallest reprojection error is selected as the final result.This method efficiently estimates both intrinsic and extrinsic camera parameters.Simulations and real-world experiments validate the high precision and effectiveness of the proposed approach.Our technique is straightforward,practical,and holds significant theoretical and practical value for rapid and reliable camera calibration.
基金supported by the National Natural Science Foundation of China(No.61972227)the Natural Science Foundation of Shandong Province(No.ZR201808160102)+4 种基金Shandong Provincial Natural Science Foundation Key Project(No.ZR2020KF015)the Key Research and Development Project of Shandong Province(No.2019GSF109112)the Science and Technology Plan for Young Talents in Colleges and Universities of Shandong Province(No.2020KJN007)the Scientific Research Studio in Colleges and Universities of Ji’nan City(No.2021GXRC092)the Science and Technology Research Program for Colleges and Universities in Shandong Province(No.KJ2018BZN029).
文摘Rain streaks in an image appear in different sizes and orientations,resulting in severe blurring and visual quality degradation.Previous CNNbased algorithms have achieved encouraging deraining results although there are certain limitations in the description of rain streaks and the restoration of scene structures in different environments.In this paper,we propose an efficient multi-scale enhancement and aggregation network(MEAN)to solve the single-image deraining problem.Considering the importance of large receptive fields and multi-scale features,we introduce a multi-scale enhanced unit(MEU)to capture longrange dependencies and exploit features at different scales to depict rain.Simultaneously,an attentive aggregation unit(AAU)is designed to utilize the informative features in spatial and channel dimensions,thereby aggregating effective information to eliminate redundant features for rich scenario details.To improve the deraining performance of the encoder–decoder network,we utilized an AAU to filter the information in the encoder network and concatenated the useful features to the decoder network,which is conducive to predicting high-quality clean images.Experimental results on synthetic datasets and real-world samples show that the proposed method achieves a significant deraining performance compared to state-of-the-art approaches.
基金supported in part by the National Natural Science Foundation of China(Grant No.62072328).
文摘Super-resolution(SR)is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the decades.The main concept of SR is to reconstruct images from low-resolution(LR)to high-resolution(HR).It is an ongoing process in image technology,through up-sampling,de-blurring,and de-noising.Convolution neural network(CNN)has been widely used to enhance the resolution of images in recent years.Several alternative methods use deep learning to improve the progress of image super-resolution based on CNN.Here,we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution.,it is also to highlight the potential applications of image super-resolution in security monitoring,medical diagnosis,microscopy image processing,satellite remote sensing,communication transmission,the digital multimedia industry and video enhancement.Finally,we present the challenges and assess future trends in super-resolution based on deep learning.
