Robust Image Restoration for Motion Blur of Image Sensors
Sensors, 2016 Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly.
Fasheng Yang +4 more
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Hyperspectral Image Reconstruction Based on Blur–Kernel–Prior and Spatial–Spectral Attention
Remote SensingGiven the problem of spatial detail loss and spectral feature degradation in hyperspectral images (HSIs) characterized as blur, often caused by noise during image acquisition, and methods of removing blur noise designed on HSIs being insufficient, we ...
Hongyu Xie +7 more
doaj +2 more sources
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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Handling Gaussian blur without deconvolution [PDF]
, 2022 The paper presents a new theory of invariants to Gaussian blur. Unlike earlier methods, the blur kernel may be arbitrary oriented, scaled and elongated.
Kostková, Jitka +3 more
core +1 more source
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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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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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
doaj +1 more source
Combined Invariants to Gaussian Blur and Affine Transformation [PDF]
, 2021 The paper presents a new theory of combined moment invariants to Gaussian blur and spatial affine transformation. The blur kernel may be arbitrary oriented, scaled and elongated.
Jan Flusser +5 more
core +1 more source
Kernel used for simulating the blur effect on confocal images.
, 2022 Kernel used for simulating the blur effect on confocal images.
Yassin Refahi (12418368) +5 more
core +1 more source