Results 151 to 160 of about 14,030 (199)
A Self-Supervised Adversarial Deblurring Face Recognition Network for Edge Devices. [PDF]
J ImagingZhang H, Kim M, Li B, Lu Y.
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Aerial image quality enhancement via correction of spatially variant aberrations. [PDF]
Sci RepDu C, Xiu J, Sun L.
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Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric. [PDF]
Sci RepPandey A +4 more
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An Enhanced YOLOv8n-Based Method for Fire Detection in Complex Scenarios. [PDF]
Sensors (Basel)Zhao X, Yu M, Xu J, Wu P, Yuan H.
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ZS4D: Zero-Shot Self-Similarity-Steered Denoiser for Volumetric Photon-Counting CT. [PDF]
IEEE Trans Radiat Plasma Med SciShi Y +6 more
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IEEE Transactions on Multimedia, 2022 Deep learning-based blind image deblurring plays an essential role in solving image blur since all existing kernels are limited in modeling the real world blur. Thus far, researchers focus on powerful models to handle the deblurring problem and achieve decent results.
Winston H Hsu
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Deep Image Deblurring: A Survey
International Journal of Computer Vision, 2022 Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published
Kaihao Zhang, Wenqi Ren, Wenhan Luo
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Image Deblurring With Image Blurring
IEEE Transactions on Image Processing, 2023Deep learning (DL) based methods for motion deblurring, taking advantage of large-scale datasets and sophisticated network structures, have reported promising results. However, two challenges still remain: existing methods usually perform well on synthetic datasets but cannot deal with complex real-world blur, and in addition, over- and under ...
Ziyao Li +4 more
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Journal of Electronic Imaging, 2014
We propose an algorithm to recover the latent image from the blurred and compressed input. In recent years, although many image deblurring algorithms have been proposed, most of the previous methods do not consider the compression effect in blurry images. Actually, it is unavoidable in practice that most of the real-world images are compressed.
Yuquan Xu, Xiyuan Hu, Silong Peng
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We propose an algorithm to recover the latent image from the blurred and compressed input. In recent years, although many image deblurring algorithms have been proposed, most of the previous methods do not consider the compression effect in blurry images. Actually, it is unavoidable in practice that most of the real-world images are compressed.
Yuquan Xu, Xiyuan Hu, Silong Peng
openaire +1 more source