Abstract
Though nature images generally consist of edges and homogeneous regions, most image deconvolution approaches will result in unnaturally sharp image estimations, since they tend to overemphasize sharpness while ignoring smoothness. To balance these two significant image properties, this paper presents a novel analysis-based image deconvolution approach based mainly on alternating minimization and variable splitting. The presented approach focuses on a new method of solving a constrained minimization problem which is derived from a universal model and is used to model the image deconvolution. By alternating minimization with variable splitting, the proposed image deconvolution model is firstly converted to an equivalent model, and then decoupled into a number of sub-problems. These sub-problems are alternately handled using a corresponding iterative method to obtain the solution for the proposed image deconvolution model. The presented approach has been demonstrated to be effective and superior to several state-of-the-art approaches for image deconvolution applications.
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This work is supported by Anhui Provincial Natural Science Foundation (No. 1608085QF150).
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Xiao, S., Zhou, Y., Wei, L. (2017). Solving a New Constrained Minimization Problem for Image Deconvolution. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10463. Springer, Cham. https://doi.org/10.1007/978-3-319-65292-4_41
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DOI: https://doi.org/10.1007/978-3-319-65292-4_41
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