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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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.
Shijie Zhang, Botian Zhou
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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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Blur Kernel Estimation by Structure Sparse Prior
Applied Sciences (Switzerland), 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 (Switzerland), 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
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Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric [PDF]
Scientific ReportsSharpness is a critical optical property of automotive cameras, measured by the spatial frequency response (SFR) within the end-of-line (EOL) test after manufacturing.
Amit Pandey +4 more
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Adaptive Image Deblurring Convolutional Neural Network with Meta-Tuning [PDF]
SensorsMotion blur is a complex phenomenon caused by the relative movement between an observed object and an imaging sensor during the exposure time, resulting in degradation in the image quality.
Quoc-Thien Ho +3 more
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Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors
IEEE Access, 2023 The goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel. Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models.
Zhe Li +3 more
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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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Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution
Information, 2023 Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version.
Asif Hussain Khan +2 more
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