Edge-preserving neural network model for image restoration
Paul G. Bao, Dianhui Wang · 2002
This paper presents a hybrid approach for image restoration with edge-preserving regularization, subband coding, and artificial neural network. The edge information is extracted from the source image as a priori knowledge to recover the details and reduce the ringing artifact of the subband coded image. The multilayer perception model is employed to implement the restoration process. A comparative study with SPIHT has been made using a set of gray-scale digital images. The experimental results have shown that the proposed approach could result in compatible performances compared with SPIHT on both objective and subjective quality for lower compression ratio subband coded image.