Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
An Unbiased Detector of Curvilinear Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear and Nonlinear Image Deblurring: A Documented Study
SIAM Journal on Numerical Analysis
Convex Optimization
Generalized low rank approximations of matrices
ICML '04 Proceedings of the twenty-first international conference on Machine learning
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Coded exposure photography: motion deblurring using fluttered shutter
ACM SIGGRAPH 2006 Papers
Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms)
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Simultaneous image formation and motion blur restoration via multiple capture
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
ACM SIGGRAPH 2008 papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Progressive inter-scale and intra-scale non-blind image deconvolution
ACM SIGGRAPH 2008 papers
Extracting the optimal dimensionality for local tensor discriminant analysis
Pattern Recognition
Fourier Analysis of the 2D Screened Poisson Equation for Gradient Domain Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Blind motion deblurring using multiple images
Journal of Computational Physics
Semi-supervised bilinear subspace learning
IEEE Transactions on Image Processing
Image deblurring using inertial measurement sensors
ACM SIGGRAPH 2010 papers
Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Classification via Local Spline Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion deblurring using hybrid imaging
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems
IEEE Transactions on Signal Processing
Wavelet Deconvolution With Noisy Eigenvalues
IEEE Transactions on Signal Processing
Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms
IEEE Transactions on Image Processing
Blind image restoration by anisotropic regularization
IEEE Transactions on Image Processing
Blur identification from vector quantizer encoder distortion
IEEE Transactions on Image Processing
A VQ-based blind image restoration algorithm
IEEE Transactions on Image Processing
Blind deconvolution of images using optimal sparse representations
IEEE Transactions on Image Processing
Blur identification by the method of generalized cross-validation
IEEE Transactions on Image Processing
Deblurring Using Regularized Locally Adaptive Kernel Regression
IEEE Transactions on Image Processing
Sparse coding for image denoising using spike and slab prior
Neurocomputing
| Hi-index | 0.01 |
This paper presents a supervised learning algorithm for image deblurring. The task is addressed into the conceptual framework of matrix regression and gradient evolution. Specifically, given pairs of blurred image patches and their corresponding clear ones, an optimization framework of matrix regression is proposed to learn a matrix mapping. For an image to be deblurred, the learned matrix mapping will be employed to map each of its image patches directly to be a new one with more sharp details. The mapped result is then analyzed in terms of edge profiles, and the image is finally deblurred in way of gradient evolution. The algorithm is fast, and easy to be implemented. Comparative experiments on diverse natural images and the applications to interactive deblurring of real-world out-of-focus images illustrate the validity of our method.