LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval
MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Probabilistic geometric approach to blind separation of time-varying mixtures
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Blind separation of position varying mixed images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Blind deconvolution of images using optimal sparse representations
IEEE Transactions on Image Processing
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We present a method for recovering source images from their non-instantaneous single path mixtures using sparse component analysis (SCA). Non-instantaneous single path mixtures refer to mixtures generated by a mixing system that spatially distorts the source images (noninstantaneous and spatially varying) without any reverberations (single path/anechoic). For example, such mixtures can be found when imaging through a semi-reflective convex medium or in various movie fade effects. Recent studies have used SCA to separately address the time/position varying and the non-instantaneous scenarios. The present study is devoted to the unified scenario. Given n anechoic mixtures (without multiple reflections) of m source images, we recover the images up to a limited number of unknown parameters. This is accomplished by means of correspondence that we establish between the sparse representation of the input mixtures. Analyzing these correspondences allows us to recover models of both spatial distortion and attenuation. We implement a staged method for recovering the spatial distortion and attenuation, in order to reduce parametric model complexity by making use of descriptor invariants and model separability. Once the models have been recovered, well known BSS tools and techniques are used in recovering the sources.