Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74200
Title: Blind image deblurring via adaptive dynamical system learning
Authors: Liu, R 
Cheng, S
Fan, X
Luo, Z
Keywords: Adaptive dynamical system
Blind image deblurring
Kernel estimation
Latent sharp image
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: Proceedings - IEEE International Conference on Multimedia and Expo, 2017, 8019475, p. 199-204 How to cite?
Abstract: Blind image deblurring is one of the main phases in most media analysis tasks. Many existing works aim to simultaneously estimate the latent image and the blur kernel under a MAP framework. However, it has been demonstrated that such joint estimation strategies may lead to the undesired trivial solution. In this paper, we propose a learnable nonlinear dynamical system to formulate the image propagation so that the blur kernel estimation can be efficiently controlled by both cues and training data. Our analysis also indicates that the proposed dynamical system is feasible on image modeling socialities. Experimental results on different benchmark image sets evaluate the effectiveness of our proposed approach.
URI: http://hdl.handle.net/10397/74200
ISBN: 9781509060672
ISSN: 1945-7871
DOI: 10.1109/ICME.2017.8019475
Appears in Collections:Conference Paper

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