Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12438
Title: An edge-preserving image reconstruction using neural network
Authors: Bao, P
Wang, D
Keywords: Edge-preserving regularization
Image restoration
Neural network
Pattern learning
Subband coding
Issue Date: 2001
Publisher: Kluwer Academic Publ
Source: Journal of mathematical imaging and vision, 2001, v. 14, no. 2, p. 117-130 How to cite?
Journal: Journal of Mathematical Imaging and Vision 
Abstract: This paper presents an image restoration model based on the implicit function theorem and edge-preserving regularization. We then apply the model on the subband-coded images using the 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 perceptron model is employed to implement the restoration process. The main merit of the presented approach is that the neural network model is massively parallel with strong robustness for the transmission noise and parameter or structure perturbation, and it can be realized by VLSI 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 experimental results showed 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.
URI: http://hdl.handle.net/10397/12438
ISSN: 0924-9907
DOI: 10.1023/A:1011207214916
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