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Motion Blurred Star Image Restoration Based on MEMS Gyroscope Aid and Blur Kernel Correction [PDF]
Sensors, 2018 Under dynamic conditions, motion blur is introduced to star images obtained by a star sensor. Motion blur affects the accuracy of the star centroid extraction and the identification of stars, further reducing the performance of the star sensor.
Shiqiang Wang +3 more
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Motion Blur Kernel Rendering Using an Inertial Sensor: Interpreting the Mechanism of a Thermal Detector [PDF]
Sensors, 2022 Various types of motion blur are frequently observed in the images captured by sensors based on thermal and photon detectors. The difference in mechanisms between thermal and photon detectors directly results in different patterns of motion blur ...
Kangil Lee, Yuseok Ban, Changick Kim
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Robust Motion Blur Kernel Estimation by Kernel Continuity Prior [PDF]
IEEE Access, 2020 The accurate kernel estimation is key to the blind motion deblurring. Many previous methods depend on the image regularization to recover strong edges in the observed image for kernel estimation.
Xueling Chen +3 more
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Blur Kernel Estimation and Non-Blind Super-Resolution for Power Equipment Infrared Images by Compressed Sensing and Adaptive Regularization [PDF]
Sensors, 2021 Infrared sensing technology is more and more widely used in the construction of power Internet of Things. However, due to cost constraints, it is difficult to achieve the large-scale installation of high-precision infrared sensors.
Hongshan Zhao +2 more
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Компьютерная оптика, 2022 A method for calculating of a blur kernel arising from the rotation of a digital camera is proposed. The rotation is measured with a three-axis gyroscope attached to the camera.
N.N. Vasilyuk
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Multiregression spatially variant blur kernel estimation based on inter‐kernel consistency
Electronics Letters, 2023 Most single‐image super‐resolution (SR) models suffer from the degradation of image restoration performance when restoring a high‐resolution (HR) image from a low‐resolution (LR) image downscaled using an unknown blur kernel. The spatially invariant blur
Min Hyuk Kim +2 more
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Blur Kernel Estimation by Structure Sparse Prior [PDF]
Applied Sciences, 2020 Blind image deblurring tries to recover a sharp version from a blurred image, where blur kernel is usually unknown. Recently, sparse representation has been successfully applied to estimate the blur kernel.
Xiaobin Yuan, Jingping Zhu, Xiaobin Li
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Applied Sciences, 2020 Image blurs are a major source of degradation in an imaging system. There are various blur types, such as motion blur and defocus blur, which reduce image quality significantly.
Haoyuan Yang, Xiuqin Su, Songmao Chen
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Motion Blur Kernel Estimation via Deep Learning
IEEE Transactions on Image Processing, 2018 The success of the state-of-the-art deblurring methods mainly depends on the restoration of sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images.
Xiangyu Xu +3 more
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Partial Deconvolution With Inaccurate Blur Kernel
IEEE Transactions on Image Processing, 2018 Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to
Dongwei Ren +4 more
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