Tensor factorization and continous wavelet transform for model-free single-frame blind image deconvolution

Model-free single-frame blind image deconvolution (BID) method is proposed by converting BID into blind source separation (BSS), whereas sources represent the original image and its spatial derivatives. Continuous wavelet transform (CWT) is used to generate multi-channel image necessary for BSS. As opposed to an approach based on the Gabor filter bank, this brings additional options in adaptability to the problem at hand: through the choice of wavelet function and variation of the scale of the CWT. BSS is performed through orthogonality constrained factorization of the 3D multichannel image tensor by means of the higher-order-orthogonal-iteration algorithm. The proposed method virtually requires no information about blurring kernel: neither model nor size of the support. The method is demonstrated on experimental gray scale images degraded by de-focusing and atmospheric turbulence. A comparable or better performance is demonstrated relative to blind Richardson-Lucy method that, however, requires a priori information about parametric model of the blur.