Rapid enhancement and compression of image data

Recently, we have presented a recursive framework for noncausal Gauss Markov Random Fields (GMRF) defined on finite lattices. This framework readdresses the issue of recursiveness in 2-D signal processing, providing the means to attain the computational advantages of recursive processing without sacrificing the noncausality of the image model. We present here results on the application of this framework to two areas, image enhancement and image compression.

[1]  J. Woods,et al.  Estimation and identification of two dimensional images , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[2]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[3]  Laveen N. Kanal,et al.  Classification of binary random patterns , 1965, IEEE Trans. Inf. Theory.

[4]  Nikhil Balram,et al.  Recursive structure of noncausal Gauss-Markov random fields , 1992, IEEE Trans. Inf. Theory.

[5]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[6]  JOHN w. WOODS,et al.  Kalman filtering in two dimensions , 1977, IEEE Trans. Inf. Theory.