An image enhancement method of an AFC-MSPCNN for Dunhuang Murals

Rongrong Jia; Jing Lian; Yutong Hou; Jibao Zhang; Jiajun Zhang

doi:10.1117/12.3035153

19 July 2024 An image enhancement method of an AFC-MSPCNN for Dunhuang Murals

Rongrong Jia, Jing Lian, Yutong Hou, Jibao Zhang, Jiajun Zhang

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132130R (2024) https://doi.org/10.1117/12.3035153
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Eckhorn obtained a model of a mammalian neuron, the pulse-coupled neural network model (PCNN), by studying neurons in the cat's visual cortex by examining their synchronized pulse oscillations. PCNN is a single-layer neural network model. The dynamic threshold of the model is adjustable, and it has the characteristics of nonlinear modulation coupling, synchronous pulse and dynamic pulse excitation, which makes PCNN model have a good effect on image feature extraction, edge information analysis, image enhancement and image segmentation. In this paper, an image enhancement model called AFC-MSPCNN is proposed for image processing. Aiming at the problems of complex structure of pulse coupled neurons, poor adaptive performance and poor processing results caused by repeated attenuation of image signal threshold in experiments. The improved model reduces the computational complexity and enhances the adaptive capability to some extent. We apply the new model AFC-MSPCNN to image enhancement and experimentally verify its good enhancement effect.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rongrong Jia, Jing Lian, Yutong Hou, Jibao Zhang, and Jiajun Zhang "An image enhancement method of an AFC-MSPCNN for Dunhuang Murals", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132130R (19 July 2024); https://doi.org/10.1117/12.3035153

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KEYWORDS

Image enhancement

Matrices

Neurons

Image processing

Image quality

Neural networks

Signal attenuation

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