Elements of information theory
Elements of information theory
Computing occluding and transparent motions
International Journal of Computer Vision
Blind source separation for convolutive mixtures
Signal Processing
Separation of real-world signals
Signal Processing - Special issue on acoustic echo and noise control
Separation of Transparent Layers using Focus
International Journal of Computer Vision
Using known motion fields for image separation in transparency
Pattern Recognition Letters
A multiscale framework for blind separation of linearly mixed signals
The Journal of Machine Learning Research
Removing photography artifacts using gradient projection and flash-exposure sampling
ACM SIGGRAPH 2005 Papers
Removing photography artifacts using gradient projection and flash-exposure sampling
ACM SIGGRAPH 2005 Papers
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Blind wideband source separation
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
Fast kernel entropy estimation and optimization
Signal Processing - Special issue: Information theoretic signal processing
Stereo matching with reflections and translucency
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Multichannel signal separation: methods and analysis
IEEE Transactions on Signal Processing
Quasi-nonparametric blind inversion of Wiener systems
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Multichannel blind separation and deconvolution of images for document analysis
IEEE Transactions on Image Processing
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Convolutive mixtures of images are common in photography of semi-reflections. They also occur in microscopy and tomography. Their formation process involves focusing on an object layer, over which defocused layers are superimposed. We seek blind source separation (BSS) of such mixtures. However, achieving this by direct optimization of mutual information is very complex and suffers from local minima. Thus, we devise an efficient approach to solve these problems. While achieving high quality image separation, we take steps that make the problem significantly simpler than a direct formulation of convolutive image mixtures. These steps make the problem practically convex, yielding a unique global solution to which convergence can be fast. The convolutive BSS problem is converted into a set of instantaneous (pointwise) problems, using a short time Fourier transform (STFT). Standard BSS solutions for instantaneous problems suffer, however, from scale and permutation ambiguities. We overcome these ambiguities by exploiting a parametric model of the defocus point spread function. Moreover, we enhance the efficiency of the approach by exploiting the sparsity of the STFT representation as a prior. We apply our algorithm to semi-reflections, and demonstrate it in experiments.