Open Access iconOpen Access

ARTICLE

crossmark

Multi-Layer Deep Sparse Representation for Biological Slice Image Inpainting

Haitao Hu1, Hongmei Ma2, Shuli Mei1,*

1 College of Information and Electrical Engineering, China Agricultural University, Beijing, 100000, China
2 Yantai Research Institute, China Agricultural University, Yantai, 264670, China

* Corresponding Author: Shuli Mei. Email: email

Computers, Materials & Continua 2023, 76(3), 3813-3832. https://doi.org/10.32604/cmc.2023.041416

Abstract

Biological slices are an effective tool for studying the physiological structure and evolution mechanism of biological systems. However, due to the complexity of preparation technology and the presence of many uncontrollable factors during the preparation processing, leads to problems such as difficulty in preparing slice images and breakage of slice images. Therefore, we proposed a biological slice image small-scale corruption inpainting algorithm with interpretability based on multi-layer deep sparse representation, achieving the high-fidelity reconstruction of slice images. We further discussed the relationship between deep convolutional neural networks and sparse representation, ensuring the high-fidelity characteristic of the algorithm first. A novel deep wavelet dictionary is proposed that can better obtain image prior and possess learnable feature. And multi-layer deep sparse representation is used to implement dictionary learning, acquiring better signal expression. Compared with methods such as NLABH, Shearlet, Partial Differential Equation (PDE), K-Singular Value Decomposition (K-SVD), Convolutional Sparse Coding, and Deep Image Prior, the proposed algorithm has better subjective reconstruction and objective evaluation with small-scale image data, which realized high-fidelity inpainting, under the condition of small-scale image data. And the -level time complexity makes the proposed algorithm practical. The proposed algorithm can be effectively extended to other cross-sectional image inpainting problems, such as magnetic resonance images, and computed tomography images.

Keywords


Cite This Article

H. Hu, H. Ma and S. Mei, "Multi-layer deep sparse representation for biological slice image inpainting," Computers, Materials & Continua, vol. 76, no.3, pp. 3813–3832, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 216

    View

  • 112

    Download

  • 0

    Like

Share Link