Results 171 to 180 of about 4,574 (198)

Blind Image Deblurring Based on Local Rank

Mobile Networks and Applications, 2019
Conventional algorithms for blind image deblurring are often inaccurate at blur kernel estimation, and the recovery effect is far from perfect. To address this, we propose a single-image blind deblurring method based on local rank. For this, we first impose adaptive threshold segmentation on a conventional local rank transform, which is subsequently ...
Li Zhu 0003, Long Jin, Jihua Zhu, Zhongyu Li 0002, Zhiqiang Tian, Huimin Lu 0001   +5 more
openaire   +1 more source

Unsupervised Blind Separation and Debluring of Mixtures of Sources

2007
In this paper we consider the problem of separating source images from linear mixtures with unknown coefficients, in presence of noise and blur. In particular, we consider as a special case the problem of estimating the Cosmic Microwave Background from galactic and extra-galactic emissions.
L. Fedeli, GERACE, Ivan, MARTINELLI, FRANCESCA   +2 more
openaire   +2 more sources

Sparse representation based blind image deblurring

2011 IEEE International Conference on Multimedia and Expo, 2011
We propose a sparse representation based blind image deblurring method. The proposed method exploits the sparsity property of natural images, by assuming that the patches from the natural images can be sparsely represented by an over-complete dictionary.
Haichao Zhang 0001, Jianchao Yang, Yanning Zhang 0001, Thomas S. Huang   +3 more
openaire   +1 more source

High-quality non-blind motion deblurring

2009 16th IEEE International Conference on Image Processing (ICIP), 2009
Traditional non-blind motion deblurring methods are sensitive to kernel estimate errors and image noise, thus suffering from either ringing artifacts, enlarged image noise, or over-smoothed image details. We introduce a robust non-blind deblurring algorithm that produces high quality results even from many challenging images with noisy kernels.
Chao Wang 0063, Lifeng Sun, Zhuoyuan Chen, Shiqiang Yang, Jianwei Zhang 0001   +4 more
openaire   +1 more source

Blind Image Deblurring Based on Dictionary Replacing

2012
Traditional image deblurring is based on deconvolution, an ill-posed problem, which is sensitive to the accuracy of the blur kernel. In this paper, we propose a blind image deblurring method based on dictionary replacing. First, we estimate the blur kernel from the blur image , and then based on the sparse representation of the image patch under over ...
Haisen Li, Yanning Zhang 0001, Feng Duan, Yu Zhu 0004   +3 more
openaire   +1 more source

Blind deblurring using adaptive image model

2016 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2016
The paper studies the blind deblurring algorithms using the proposed adaptive image model which is based on the random line field. Both algorithms are constructed following the Bayesian framework. The deblurring results of the proposed algorithm are compared with those of the deblurring algorithms in the literature.
Ngoc-Thuy Le, Ngoc-Minh Nguyen
openaire   +1 more source

Blind Deblurring Using Discriminative Image Smoothing

2018
This paper aims to exploit the full potential of gradient-based methods, attempting to explore a simple, robust yet discriminative image prior for blind deblurring. The specific contributions are three-fold: Above all, a pure gradient-based heavy-tailed model is proposed as a generalized integration of the normalized sparsity and the relative total ...
Wenze Shao, Yunzhi Lin, Bingkun Bao, Liqian Wang, Qi Ge, Haibo Li 0001   +5 more
openaire   +1 more source

Blind Image Deblurring Using Adaptive Priors

2018
For blind image deblurring, a good prior knowledge can guide the maximum a posterior (MAP) based algorithms to be away from the trivial solution. Therefore, many existing methods focus on designing effective priors to constrain the solution space. However, blind deconvolution with fixed priors is not robust.
Bingwang Zhang, Risheng Liu, Haojie Li, Qi Yuan, Xin Fan 0001, Zhongxuan Luo   +5 more
openaire   +1 more source

Spectral Non-gaussianity for Blind Image Deblurring

2011
A blind image deblurring method based on a new nongaussianity measure and independent component analysis is presented. The scheme assumes independency among source signals (image and filter function) in the frequency domain. According to the Central Limit Theorem the blurred image becomes more Gaussian.
Aftab Khan, Hujun Yin
openaire   +1 more source

Deep Idempotent Network for Efficient Single Image Blind Deblurring

IEEE Transactions on Circuits and Systems for Video Technology, 2023
Zhexiong Wan, Yuchao Dai, Xin Yu
exaly