Murals inpainting based on generalized
regression neural network

REN Xiao-kang，CHEN Pei-lin

（College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China）

Received:2016-05-18 Revised:2016-06-23 Online:2017-10-25 Published:2017-10-25

Abstract:

We propose a digital inpainting and protection method for Dunghuang murals through the application of the

generalized regression neural network. The damaged mural images are denoised with the anisotropic diffusion

method. Morphological dilation operator is applied to extract the boundary pixels in the damaged region. The

sample blocks of pixels similar to those around the boundary of the damaged region serve as input training

samples of the generalized regression neural network. The pixel values of the sample blocks are input into

the generalized regression neural network by the zigzag scanning. Adaptive smoothing parameters, in addition,

are also used. Finally, an approximate inpainting model is obtained, which can predict the information of the

pixels in the damaged region. Experimental results indicate that the proposed method is effective in the

digital inpainting of murals.

Key words: mural inpainting, generalized regression neural network, morphological dilation；anisotropic diffusion

REN Xiao-kang，CHEN Pei-lin.

Murals inpainting based on generalized

regression neural network

[J]. Computer Engineering & Science.

[1]	REN Xiaokang,DENG Linkai. Murals inpainting of the wavelet texture description algorithm based on scale space [J]. J4, 2014, 36(11): 2191-2195.
[2]	REN Xiaokang,DENG Linkai. Murals inpainting based on color clustering image segmentation and the improved FMM algorithm [J]. J4, 2014, 36(02): 298-302.

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