Edge-preserved neural network model for image restoration

The University of Hong Kong, Hong Kong, 00, Hong Kong
Journal of Electronic Imaging (Impact Factor: 0.67). 07/2001; 10(3):735-743. DOI: 10.1117/1.1380389
Source: DBLP


This paper presents a combined approach for image restoration with edge-preserving regularization, subband coding, and artificial neural network. The edge information is detected from the source image as a prior knowledge to recover the details and reduce the ringing artifact of the subband coded image. The multilayer perceptron model is employed to implement the restoration of images. The main merit of the presented approach is that the neural network model is massively parallel with stronger robustness for transmission noise and parameter or structure perturbation, and it can be realized by very large scale integrated technologies for real-time applications. To evaluate the performance of the proposed approach, a comparative study with the set partitioning in hierarchical tree (SPIHT) has been made by using a set of gray-scale digital images. The experiment has shown that the proposed approach could result in considerably better performances compared with SPIHT on both objective and subjective quality for lower compression ratio subband coded image.

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