Recovery of blocky images from noisy and blurred data
SIAM Journal on Applied Mathematics
A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions
SIAM Journal on Scientific Computing
Motion-Based Motion Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two motion-blurred images are better than one
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Coded exposure photography: motion deblurring using fluttered shutter
ACM SIGGRAPH 2006 Papers
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Deconvolution: a wavelet frame approach
Numerische Mathematik
Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
SIAM Journal on Imaging Sciences
Linearized Bregman Iterations for Frame-Based Image Deblurring
SIAM Journal on Imaging Sciences
Blur identification using the bispectrum
IEEE Transactions on Signal Processing
Total variation blind deconvolution
IEEE Transactions on Image Processing
Iterative Regularization and Nonlinear Inverse Scale Space Applied to Wavelet-Based Denoising
IEEE Transactions on Image Processing
A genetic algorithm for the identification and segmentation of known motion-blurred objects
ACS'09 Proceedings of the 9th WSEAS international conference on Applied computer science
Analysis and Generalizations of the Linearized Bregman Method
SIAM Journal on Imaging Sciences
Image deblurring with matrix regression and gradient evolution
Pattern Recognition
Video deblurring for hand-held cameras using patch-based synthesis
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure
Advanced Engineering Informatics
Hi-index | 31.45 |
Recovery of degraded images due to motion blurring is a challenging problem in digital imaging. Most existing techniques on blind deblurring are not capable of removing complex motion blurring from the blurred images of complex structures. One promising approach is to recover the clear image using multiple images captured for the scene. However, in practice it is observed that such a multi-frame approach can recover a high-quality clear image of the scene only after multiple blurred image frames are accurately aligned during pre-processing, which is a very challenging task even with user interactions. In this paper, by exploring the sparsity of the motion blur kernel and the clear image under certain domains, we propose an alternative iteration approach to simultaneously identify the blur kernels of given blurred images and restore a clear image. Our proposed approach is not only robust to image formation noises, but is also robust to the alignment errors among multiple images. A modified version of linearized Bregman iteration is then developed to efficiently solve the resulting minimization problem. Experiments show that our proposed algorithm is capable of accurately estimating the blur kernels of complex camera motions with minimal requirements on the accuracy of image alignment. As a result, our method is capable of automatically recovering a high-quality clear image from multiple blurred images.