Image Modeling And Restoration By Higher-order Statistics Based Inverse Filters

Abstract

This paper presents image modeling and restoration by higher-order statistics based 2-D inverse filters. A given original image +(m, n) is processed by an optimum inverse filter v(m, n) which is designed by maximizing cumulant based criteria J r ,m = ICmlr/ICrlm where r is even, m > T 2 2 and C,,, (Cr) denotes mthorder (rth-order cumulant of the output e(m,n) of be modeled as the output of a linear shift-invariant (LSI) system h(m, n) driven by e(m, n) where h(m, n) is a stable inverse filter of v(m,n) . When a blurred image y(m,n) = t(m,n) * g(m,n,) rather than the original image z(m, n) is given, t (m, n) can be restored by first estimating e (m,n) using the previous inverse filter criteria and then obtain t(m,n) = e(m, n) * h(m, n). Some experimental results are provided to support the proposed image modeling and restoration method. the 2-D inverse A Iter. The original image z (m, n) can