Image inpainting based on directional Gaussian graph model using multi-head reference

Zhonghao Zhang; Lihong Ma

doi:10.1117/12.2631560

1 May 2022 Image inpainting based on directional Gaussian graph model using multi-head reference

Zhonghao Zhang, Lihong Ma

Proceedings Volume 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021); 1217113 (2022) https://doi.org/10.1117/12.2631560
Event: Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 2021, Shanghai, China

Abstract

This paper aims to repair missing regions which are corrupted along arbitrary directions. It presents a mixed image inpainting method based on Markov random field. By using Belief Propagation scheme in low gray-levels and alternately suggesting a directional Gaussian Graphical model (DGGM) for multiple references in a high-level range, it gains a balance between the model accuracy and the computation complexity in realization. On the basis of an existing method in [1], it improves the method in high level inpainting for the task under small train sets and large corrupted regions, by introducing these multi-head reference clues. Experimental results are given, the inpainting quality of different kinds of images with different sizes and contents under different parameter settings are compared in metrics of the peak signal noise ratio and the structural similarity index. The significance of parameter settings and the efficient computational cost could demonstrate the feasibility of this method.

Citation Download Citation

Zhonghao Zhang and Lihong Ma "Image inpainting based on directional Gaussian graph model using multi-head reference", Proc. SPIE 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 1217113 (1 May 2022); https://doi.org/10.1117/12.2631560

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KEYWORDS

Image quality

Contamination

Image restoration

Head

Magnetorheological finishing

Image resolution

Diffusion

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