Results 11 to 20 of about 512,229 (281)
Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution [PDF]
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
doaj +3 more sources
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
doaj +2 more sources
Convergence Analysis of MAP Based Blur Kernel Estimation [PDF]
2017 IEEE International Conference on Computer Vision (ICCV), 2017 One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their ...
Sunghyun Cho, Seungyong Lee 0001
openaire +4 more sources
Blur Unblurred—A Mini Tutorial [PDF]
i-Perception, 2018 Optical blur from defocus is quite frequently considered as equivalent to low-pass filtering. Yet that belief, although not entirely wrong, is inaccurate.
Hans Strasburger +2 more
doaj +2 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
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
doaj +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
doaj +1 more source