{"title":"非均匀相机抖动去模糊中的 IMU 辅助精确模糊内核再估计。","authors":"Jianxiang Rong;Hua Huang;Jia Li","doi":"10.1109/TIP.2024.3411819","DOIUrl":null,"url":null,"abstract":"Image deblurring for camera shake is a highly regarded problem in the field of computer vision. A promising solution is the patch-wise non-uniform image deblurring algorithms, where a linear transformation model is typically established between different blur kernels to re-estimate poorly estimated blur kernels. However, the linear model struggles to effectively describe the nonlinear transformation relationships between blur kernels. A key observation is that the inertial measurement unit (IMU) provides motion data of the camera, which is helpful in describing the landmarks of the blur kernel. This paper presents a new IMU-assisted method for the re-estimation of poorly estimated blur kernels. This method establishes a nonlinear transformation relationship model between blur kernels of different patches using IMU motion data. Subsequently, an optimization problem is applied to re-estimate poorly estimated blur kernels by incorporating this relationship model with neighboring well-estimated kernels. Experimental results demonstrate that this blur kernel re-estimation method outperforms existing methods.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IMU-Assisted Accurate Blur Kernel Re-Estimation in Non-Uniform Camera Shake Deblurring\",\"authors\":\"Jianxiang Rong;Hua Huang;Jia Li\",\"doi\":\"10.1109/TIP.2024.3411819\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image deblurring for camera shake is a highly regarded problem in the field of computer vision. A promising solution is the patch-wise non-uniform image deblurring algorithms, where a linear transformation model is typically established between different blur kernels to re-estimate poorly estimated blur kernels. However, the linear model struggles to effectively describe the nonlinear transformation relationships between blur kernels. A key observation is that the inertial measurement unit (IMU) provides motion data of the camera, which is helpful in describing the landmarks of the blur kernel. This paper presents a new IMU-assisted method for the re-estimation of poorly estimated blur kernels. This method establishes a nonlinear transformation relationship model between blur kernels of different patches using IMU motion data. Subsequently, an optimization problem is applied to re-estimate poorly estimated blur kernels by incorporating this relationship model with neighboring well-estimated kernels. Experimental results demonstrate that this blur kernel re-estimation method outperforms existing methods.\",\"PeriodicalId\":94032,\"journal\":{\"name\":\"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10558778/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10558778/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
IMU-Assisted Accurate Blur Kernel Re-Estimation in Non-Uniform Camera Shake Deblurring
Image deblurring for camera shake is a highly regarded problem in the field of computer vision. A promising solution is the patch-wise non-uniform image deblurring algorithms, where a linear transformation model is typically established between different blur kernels to re-estimate poorly estimated blur kernels. However, the linear model struggles to effectively describe the nonlinear transformation relationships between blur kernels. A key observation is that the inertial measurement unit (IMU) provides motion data of the camera, which is helpful in describing the landmarks of the blur kernel. This paper presents a new IMU-assisted method for the re-estimation of poorly estimated blur kernels. This method establishes a nonlinear transformation relationship model between blur kernels of different patches using IMU motion data. Subsequently, an optimization problem is applied to re-estimate poorly estimated blur kernels by incorporating this relationship model with neighboring well-estimated kernels. Experimental results demonstrate that this blur kernel re-estimation method outperforms existing methods.