Edge-preserved neural network model for image restoration

Paul Bao; Dianhui Wang

doi:10.1117/1.1380389

1 July 2001 Edge-preserved neural network model for image restoration

Paul Bao, Dianhui Wang

Journal of Electronic Imaging, Vol. 10, Issue 3, (July 2001). https://doi.org/10.1117/1.1380389

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 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 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 realtime 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.

©(2001) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Paul Bao and Dianhui Wang "Edge-preserved neural network model for image restoration," Journal of Electronic Imaging 10(3), (1 July 2001). https://doi.org/10.1117/1.1380389

Published: 1 July 2001

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