title: Blind Poissonian images deconvolution with framelet regularization.
abstract: We propose a maximum a posteriori blind Poissonian images deconvolution approach with framelet regularization for the image and total variation (TV) regularization for the point spread function. Compared with the TV based methods, our algorithm not only suppresses noise effectively but also recovers edges and detailed information. Moreover, the split Bregman method is exploited to solve the resulting minimization problem. Comparative results on both simulated and real images are reported.
authors: Science and Technology on Multi-spectral Information Processing Laboratory, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
authors: Houzhang
authors: H
authors: Fang
authors: Luxin
authors: L
authors: Yan
authors: Hai
authors: H
authors: Liu
authors: Yi
authors: Y
authors: Chang
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016081:Moon; D016012:Poisson Distribution; D012987:Soil; D014057:Tomography, X-Ray Computed
chemical_list: D012987:Soil