Image restoration based on generalized finite automata encoded edge preserving regularization

摘要

We present an edge preserving regularization scheme for the restoration of degraded images compressed by the lossy compression method based on Generalized Finite Automata edge encoding and the Iterative Constrained Least Square Regularization technique. In this scheme, the degraded image reconstructed from lossy image compressions is treated as the input to the image restoration process. The edge information extracted from the source image is utilized as a priori knowledge for the subsequent reconstruction. In order to compromise the overall bit rate incurred by the additional edge information, a generalized finite automata encoding technique is adopted to encode the bit-planes of the edge image. The generalized finite automata method ensures an efficient and adaptive image-independent encoding of the edge image. The experiment has shown that the proposed scheme could significantly improve both the objective and subjective quality of the reconstructed image over that of the set partitioning in hierarchical trees by recovering more image details and edge structures under the same bit rates.