Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62733
Title: Vector quantized pattern learning for neural network-based image restoration
Authors: Bao, PAUL GE
Lee, NT
Issue Date: 2001
Publisher: Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy
Source: Image processing & communications, 2001, v. 6, no. 3-4, p. 61-73 How to cite?
Journal: Image processing & communications 
Abstract: This paper presents a hybrid scheme for image restoration with edge-preserving regularization and artificial neural network based on vector quantized pattern learning. 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 spatially independent vector patterns are generated from source images using vector quantization to de-correlate the image patterns for more effective and efficient pattern learning and to minimize the number of training patterns while retaining the representativeness of the training patterns. The vector-quantized patterns are then used to train the multilayer perceptron model for the restoration process. To evaluate the performance of the proposed scheme, a comparative study with the set partitioning in hierarchical tree (SPIHT) and the full pattern trained NN has been conducted using a set of gray-scale digital images.The experimental results have drown that the proposed scheme could result in better performances compared with SPIHT on both objective and subjective quality for lower compression ratio subband coded image.
URI: http://hdl.handle.net/10397/62733
ISSN: 1425-140X
EISSN: 2199-6199
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