PSF estimation for defocus blurred image based on quantum back-propagation neural network

Kun Gao; Yan Zhang; Xiao-guang Shao; Ying-hui Liu; Guoqiang Ni

doi:10.1117/12.868866

9 November 2010 PSF estimation for defocus blurred image based on quantum back-propagation neural network

Kun Gao, Yan Zhang, Xiao-guang Shao, Ying-hui Liu, Guoqiang Ni

Proceedings Volume 7850, Optoelectronic Imaging and Multimedia Technology; 785018 (2010) https://doi.org/10.1117/12.868866
Event: Photonics Asia 2010, 2010, Beijing, China

Abstract

Images obtained by an aberration-free system are defocused blur due to motion in depth and/or zooming. The precondition of restoring the degraded image is to estimate point spread function (PSF) of the imaging system as precisely as possible. But it is difficult to identify the analytic model of PSF precisely due to the complexity of the degradation process. Inspired by the similarity between the quantum process and imaging process in the probability and statistics fields, one reformed multilayer quantum neural network (QNN) is proposed to estimate PSF of the defocus blurred image. Different from the conventional artificial neural network (ANN), an improved quantum neuron model is used in the hidden layer instead, which introduces a 2-bit controlled NOT quantum gate to control output and adopts 2 texture and edge features as the input vectors. The supervised back-propagation learning rule is adopted to train network based on training sets from the historical images. Test results show that this method owns excellent features of high precision and strong generalization ability.

Citation Download Citation

Kun Gao, Yan Zhang, Xiao-guang Shao, Ying-hui Liu, and Guoqiang Ni "PSF estimation for defocus blurred image based on quantum back-propagation neural network", Proc. SPIE 7850, Optoelectronic Imaging and Multimedia Technology, 785018 (9 November 2010); https://doi.org/10.1117/12.868866

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