Results 21 to 30 of about 557,712 (297)
Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
Applied Sciences, 2022 Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images.
Shanshan Liu +2 more
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Super Resolution with Kernel Estimation and Dual Attention Mechanism
Information, 2020 Convolutional Neural Networks (CNN) have led to promising performance in super-resolution (SR). Most SR methods are trained and evaluated on predefined blur kernel datasets (e.g., bicubic).
Huan Liang +4 more
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BLUR KERNEL’S EFFECT ON PERFORMANCE OF SINGLE-FRAME SUPER-RESOLUTION ALGORITHMS FOR SPATIALLY ENHANCING HYPERION AND PRISMA DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Single-frame super-resolution (SFSR) achieves the goal of generating a high-resolution image from a single low-resolution input in a three-step process, namely, noise removal, up-sampling and deblurring.
K. Mishra, R. D. Garg
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High energy flash X‐ray image restoration using region extrema and kernel optimization
IET Image Processing, 2021 The quality of high energy flash X‐ray images is crucial to the high‐precision diagnosis of object density. High energy flash X‐ray radiography is susceptible to the system blur, which usually causes the poor quality of static images. In response to this,
Xiaolin Wang, Qingwu Li, Jinxin Xu
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A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images
Sensors, 2021 Super-resolution (SR) algorithms based on deep learning have dominated in various tasks, including medical imaging, street view surveillance and face recognition. In the remote sensing field, most of the current SR methods utilize the low-resolution (LR)
Li Yan, Kun Chang
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Modeling nonstationary lens blur using eigen blur kernels for restoration
Optics Express, 2020 Images acquired through a lens show nonstationary blur due to defocus and optical aberrations. This paper presents a method for accurately modeling nonstationary lens blur using eigen blur kernels obtained from samples of blur kernels through principal component analysis. Pixelwise variant nonstationary lens blur is expressed as a linear combination of
Moonsung Gwak, Seungjoon Yang
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A Real-Time Star Tailing Removal Method Based on Fast Blur Kernel Estimations
, 2021 The number of star points and the accuracy of star centroid extraction are the key factors that affect the performance of the star sensor under high dynamic conditions.
Ya-Li Hou +4 more
semanticscholar +1 more source
Sensors, 2021 Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind ...
Yan Wang +3 more
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Pixel-Level Kernel Estimation for Blind Super-Resolution
IEEE Access, 2021 Throughout the past several years, deep learning-based models have achieved success in super-resolution (SR). The majority of these works assume that low-resolution (LR) images are ‘uniformly’ degraded from their corresponding high ...
Jaihyun Lew, Euiyeon Kim, Jae-Pil Heo
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Investigation of blur kernel of terahertz images
Lithuanian Journal of Physics, 2023 The paper discusses issues of digital processing of terahertz images. It is shown that despite the improvement of the hardware part of imaging setups, the acquired images still often have a low resolution and suffer from noise and blurring effects. Thus, to improve their visual quality, it is advisable to use special digital processing methods.
Viktoriia Abramova +5 more
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