Results 151 to 160 of about 13,900 (206)
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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A deep convolutional neural network trained for lightness constancy is susceptible to lightness illusions. [PDF]
J VisPatel J +6 more
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An Integrated and Robust Vision System for Internal and External Thread Defect Detection with Adversarial Defense. [PDF]
Sensors (Basel)Fu L, Li L, Zhang G, Jiang Z.
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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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Surface-Aware Blind Image Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Blind image deblurring is a conundrum because there are infinitely many pairs of latent image and blur kernel. To get a stable and reasonable deblurred image, proper prior knowledge of the latent image and the blur kernel is urgently required. Different from the recent works on the statistical observations of the difference between the blurred image ...
Jun Liu, Ming Yan, Tieyong Zeng
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2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0-1 loss. A novel bound allows the calculation of very high-dimensional integrals in closed form.
Meiguang Jin, Stefan Roth, Paolo Favaro
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We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0-1 loss. A novel bound allows the calculation of very high-dimensional integrals in closed form.
Meiguang Jin, Stefan Roth, Paolo Favaro
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Plenoptic Image Motion Deblurring
IEEE Transactions on Image Processing, 2018We propose a method to remove motion blur in a single light field captured with a moving plenoptic camera. Since motion is unknown, we resort to a blind deconvolution formulation, where one aims to identify both the blur point spread function and the latent sharp image.
Paramanand Chandramouli +3 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