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论文类型：会议论文

发表时间：2017-08-23

收录刊物：EI

卷号：819

页面范围：108-115

摘要：For blind image deblurring problem, regularization term met-hod is effective and efficient. Many existing approaches usually rely on carefully designed regularization terms and handcrafted parameter tuning to obtain satisfactory solution. It is complex and difficult. In this paper, we proposed a novel learning-based blind deconvolution method. We learn a Multi-Scale Shrinkage Fields model (MSSF). At each scale, we obtain the nonlinear functions and parameters through the data-driven way. Our method achieved strong robustness against others. It was evaluated on several widely-used natural image deblurring benchmarks, and achieved competitive results. © Springer Nature Singapore Pte Ltd. 2018.