Learning multi-scale shrinkage fields for blind image deblurring

Abstract

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.