Image modeling and restoration—Information fusion, Set-theoretic methods, and the Maximum entropy principle
Prakash Ishwar · AIP conference proceedings · 2001
Several powerful, but heuristic techniques in recent image denoising literature have used overcomplete image representations. We present a general framework for fusing information from multiple representations based on fundamental statistical estimation principles where, information about image attributes from multiple wavelet transforms is incorporated as moment constraints on the underlying image prior. Our method constructs the maximum entropy distribution consistent with these moment constraints. A maximum a posteriori (MAP) image restoration algorithm based on this maximum entropy prior is developed. We also explore a fundamental equivalence between the stochastic setting of multiple-domain restoration and its deterministic set-theoretic counterpart. The insights gained by this analysis allow us to derive a state-of-the-art denoising algorithm.