Projection defocus analysis for scene capture and image display
ACM SIGGRAPH 2006 Papers
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilevel algorithm for a Poisson noise removal model with total-variation regularization
International Journal of Computer Mathematics - Fast Iterative and Preconditioning Methods for Linear and Non-Linear Systems
Real Aperture Axial Stereo: Solving for Correspondences in Blur
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Three-stage motion deblurring from a video
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Combinatorial optimization for electrode labeling of EEG caps
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Markov random field based binarization for hand-held devices captured document images
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Multiclass image labeling with semidefinite programming
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Approximate MRF inference using bounded treewidth subgraphs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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The goal of deconvolution is to recover an image x from its convolution with a known blurring function. This is equivalent to inverting the linear system y = Hx. In this paper we consider the generalized problem where the system matrix H is an arbitrary non-negative matrix. Linear inverse problems can be solved by adding a regularization term to impose spatial smoothness. To avoid oversmoothing, the regularization term must preserve discontinuities; this results in a particularly challenging energy minimization problem. Where H is diagonal, as occurs in image denoising, the energy function can be solved by techniques such as graph cuts, which have proven to be very effective for problems in early vision. When H is non-diagonal, however, the data cost for a pixel to have a intensity depends on the hypothesized intensities of nearby pixels, so existing graph cut methods cannot be applied. This paper shows how to use graph cuts to obtain a discontinuity-preserving solution to a linear inverse system with an arbitrary non-negative system matrix. We use a dynamically chosen approximation to the energy which can be minimized by graph cuts; minimizing this approximation also decreases the original energy. Experimental results are shown for MRI reconstruction from fourier data.