Probabilistic Solution of Ill-Posed Problems in Computational Vision
本文回顾了标准正则化理论在计算视觉逆问题中的局限，提出新的贝叶斯方法，并给出高效算法及并行实现。
Abstract Computational vision is a set of inverse problems. We review standard regularization theory, discuss its limitations, and present new stochastic (in particular, Bayesian) methods for their solution. We derive efficient algorithms and describe parallel implementations on digital parallel SIMD architectures, as well as a new class of parallel hybrid computers.