Results 171 to 180 of about 9,140,641 (221)

Unified-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images. [PDF]

J Imaging
Zhang Y.
europepmc   +1 more source

Neural network enabled wide field-of-view imaging with hyperbolic metalenses. [PDF]

Nanophotonics
Yeo J, Sharma DK, Srivastava S, Huang A, Lassalle E, Khaidarov E, Lai KH, Fu YH, Loh ND, Kuznetsov AI, Paniagua-Dominguez R.   +10 more
europepmc   +1 more source

Real-world defocus deblurring via score-based diffusion models. [PDF]

Sci Rep
Li Y, Fang H, Lei X, Wang Q, Hu G, Dong J, Li Z, Lin J, Liu Q, Song X.   +9 more
europepmc   +1 more source

Visual-Inertial Fusion-Based Restoration of Image Degradation in High-Dynamic Scenes with Rolling Shutter Cameras. [PDF]

Sensors (Basel)
Ye J, Luo C, Wu Q, Ye Y, Li S, Chen Y, Li A.   +6 more
europepmc   +1 more source

Multi-scale diffusion model for underwater image restoration and enhancement. [PDF]

PLoS One
Fang Y, Li Q, Wang K.
europepmc   +1 more source

A Self-Supervised Adversarial Deblurring Face Recognition Network for Edge Devices. [PDF]

J Imaging
Zhang H, Kim M, Li B, Lu Y.
europepmc   +1 more source

Noise-Blind Image Deblurring [PDF]

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
openaire   +4 more sources

Plenoptic Image Motion Deblurring

IEEE Transactions on Image Processing, 2018
We 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, Meiguang Jin, Daniele Perrone, Paolo Favaro   +3 more
openaire   +4 more sources

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Image Deblurring With Image Blurring

IEEE Transactions on Image Processing, 2023
Deep 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, Zhi Gao 0005, Han Yi, Yu Fu, Boan Chen   +4 more
openaire   +2 more sources

Compressed image deblurring

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
openaire   +2 more sources