The image processing handbook
Blind Source Separation Using Temporal Predictability
Neural Computation
Iterative Identification and Restoration of Images (The International Series in Engineering and Computer Science)
Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Blind signal deconvolution as an instantaneous blind separation of statistically dependent sources
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Csiszár’s divergences for non-negative matrix factorization: family of new algorithms
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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In our previous research, we applied independent component analysis (ICA) for the restoration of image sequences degraded by atmospheric turbulence. The original high-resolution image and turbulent sources were considered independent sources from which the degraded image is composed of. Although the result was promising, the assumption of source independence may not be true in practice. In this paper, we propose to apply the concept of dependent component analysis (DCA), which can relax the independence assumption, to image restoration. In addition, the restored image can be further enhanced by employing a recently developed Gabor-filter-bank-based single channel blind image deconvolution algorithm. Both simulated and real data experiments demonstrate that DCA outperforms ICA, resulting in the flexibility in the use of adjacent image frames. The contribution of this research is to convert the original multi-frame blind deconvolution problem into blind source separation problem without the assumption on source independence; as a result, there is no a priori information, such as sensor bandwidth, point-spread-function, or statistics of source images, that is required.