What is the goal of sensory coding?
Neural Computation
A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions
SIAM Journal on Scientific Computing
A Note on Antireflective Boundary Conditions and Fast Deblurring Models
SIAM Journal on Scientific Computing
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Blind restoration of atmospherically degraded images by automatic best step-edge detection
Pattern Recognition Letters
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
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
SIAM Journal on Imaging Sciences
Antireflective boundary conditions for deblurring problems
Journal of Electrical and Computer Engineering - Special issue on iterative signal processing in communications
Information Sciences: an International Journal
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Majorization–Minimization Algorithms for Wavelet-Based Image Restoration
IEEE Transactions on Image Processing
Fine-Granularity and Spatially-Adaptive Regularization for Projection-Based Image Deblurring
IEEE Transactions on Image Processing
| Hi-index | 7.29 |
In image deconvolution, various boundary conditions (BC) based deconvolution methods have been proposed to reduce boundary artifacts. However, most of them are not considering the accuracy of BC due to computation limitation. In this paper, we propose a BC based deconvolution framework, which considers the convolution matrix as a product of partial convolution matrix and boundary condition matrix. By computing the adjoint matrix of boundary condition matrix, we can solve this large linear system with conjugate gradient algorithm. With this framework, we can easily derive two efficient non-blind image deconvolution algorithms, which treat the borders of image as repeated instances of the edge pixel values and unknown variables, respectively. Experiments on synthetic data and real data are both presented to show the performance of various BCs. Our conclusion is that undetermined BC usually has the best performance, and repeated BC outperforms undetermined BC if the latent image has high local similarity around the boundary.