This paper presents an image restoration model based on the implicit functi
on theorem and edge-preserving regularization. We then apply the model on t
he subband-coded images using the artificial neural network. The edge infor
mation is extracted from the source image as a priori nowledge to recover t
he details and reduce the ringing artifact of the subband-coded image. The
multilayer perceptron model is employed to implement the restoration proces
s. The main merit of the presented approach is that the neural network mode
l is massively parallel with strong robustness for the transmission noise a
nd parameter or structure perturbation, and it can be realized by VLSI tech
nologies for real-time applications. To evaluate the performance of the pro
posed approach, a comparative study with the set partitioning in hierarchic
al tree (SPIHT) has been made by using a set of gray-scale digital images.
The experimental results showed that the proposed approach could result in
compatible performances compared with SPIHT on both objective and subjectiv
e quality for lower compression ratio subband coded image.