Tensorial restoration neural network for digital textual image inpainting

Yingyin Fan; Jie Tan; Wanyi Li; Ling Zou

doi:10.1117/12.3038548

23 August 2024 Tensorial restoration neural network for digital textual image inpainting

Yingyin Fan, Jie Tan, Wanyi Li, Ling Zou

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132500B (2024) https://doi.org/10.1117/12.3038548
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

A new neural network model called Tensorial Restoration Neural Network Model (TRNNM) is proposed, which can repair the noise, blur, and information missing of digital textual images. It has a different structure from traditional classification convolutional neural networks which have deconvolution layer inside to perform dimensionality enhancement and inpainting on the repaired image data. After experimental verification of the proposed network model, the processed handwritten images are showing good restoration performance, which is much better than some traditional methods.

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

Citation Download Citation

Yingyin Fan, Jie Tan, Wanyi Li, and Ling Zou "Tensorial restoration neural network for digital textual image inpainting", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132500B (23 August 2024); https://doi.org/10.1117/12.3038548

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