engleski

3D Tensor Factorization Approach to Single-frame Model-free Blind Image Deconvolution

By applying bank of 2D Gabor filters to blurred image single frame blind image deconvolution (SF BID) is formulated as 3D tensor factorization (TF) problem with the key contribution that neither origin nor size of the spatially invariant blurring kernel is required to be known or estimated. Mixing matrix, original image and its spatial derivatives are identified from the factors in Tucker3 model of the multi-channel version of the blurred image. Previous approaches to 2D Gabor filter bank-based SF BID relied on 2D representation of the multi-channel version of the blurred image and matrix factorization methods such as nonnegative matrix factorization (NMF) and independent component analysis (ICA). Unlike matrix factorization-based methods 3D TF preserves local structure in the image. Moreover, 3D TF based on PARAFAC model is unique up to permutation and scale under very mild conditions. To achieve this, NMF and ICA respectively require enforcement of sparseness and statistical independence constraints on the original image and its spatial derivatives. These constraints are generally not satisfied. The 3D TF-based SF BID method is demonstrated on experimental defocused RGB image.

Deconvolution ; Image reconstruction techniques ; Inverse problems ; Superresolution ; Three-dimensional image processing

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