Results 161 to 170 of about 14,030 (199)
Some of the next articles are maybe not open access.
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 +1 more source
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 +1 more source
Blind Deblurring for Saturated Images
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that pixels around saturated regions are not conforming to the commonly used linear blur model.
Liang Chen 0026 +4 more
openaire +1 more source
Recent advances in image deblurring
SIGGRAPH Asia 2013 Courses, 2013Motion blur is a common artifact that produces disappointing blurry images with inevitable information loss. Due to the nature of imaging sensors that accumulates incoming lights, a motion blurred image will be obtained if the camera sensor moves during exposure.
Seungyong Lee 0001, Sunghyun Cho
openaire +1 more source
Class-Specific Image Deblurring
2015 IEEE International Conference on Computer Vision (ICCV), 2015In image deblurring, a fundamental problem is that the blur kernel suppresses a number of spatial frequencies that are difficult to recover reliably. In this paper, we explore the potential of a class-specific image prior for recovering spatial frequencies attenuated by the blurring process.
Saeed Anwar +2 more
openaire +1 more source
Simple method for image deblurring
Applied Optics, 1983A simple method for restoration of linearly blurred images is described utilizing a weak reference beam, coherence spoiling in one dimension, and a provision for scale change. Experimental results are given.
Y G, Jiang, Y R, Xu
openaire +2 more sources
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
openaire +2 more sources
Texture-Preserving Image Deblurring
IEEE Signal Processing Letters, 2010In this letter, a two-step texture-preserving image deblurring method is proposed. This method restores the blurred image in the frequency domain to obtain a noisy result with minimal loss of signal components, followed by a modified non-local means (NLM) filter to attenuate the leaked colored noise.
Fen Chen, Xiaojun Huang, Wufan Chen
openaire +1 more source
Debluring Low-Resolution Images
2017The recent years have witnessed significant advances in image deblurring. In general, the success of deblurring methods depends heavily on extraction of salient structures from a blurry image for kernel estimation. Most deblurring methods often operate on high-resolution images where contours or edges can be extracted for kernel estimation.
Jin-shan Pan +3 more
openaire +1 more source
Adaptive deblurring of noisy images
SPIE Proceedings, 2006We propose a practical sensor deblurring filtering method for images that are contaminated with noise. A sensor blurring function is usually modeled via a Gaussian-like function having a bell shape. The straightforward inverse function results in the magnification of noise at high frequencies.
S Susan, Young +3 more
openaire +2 more sources
2014
Recovering a sharp version of an input blurred image is challenging in computational photography and digital image processing. Recent progresses have been made in algorithms to address the ill-posedness of the problem. Yet, the results are imperfect. This thesis presents two approaches that explore latent priors from observations to provide a better ...
openaire +2 more sources
Recovering a sharp version of an input blurred image is challenging in computational photography and digital image processing. Recent progresses have been made in algorithms to address the ill-posedness of the problem. Yet, the results are imperfect. This thesis presents two approaches that explore latent priors from observations to provide a better ...
openaire +2 more sources