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STSRNet: Self-Texture Transfer Super-Resolution and Refocusing Network
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DOI：10.1109/TMI.2021.3112923.png)
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摘要
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Biomedical microscopy images with high-resolution (HR) and axial information can help analysis and diagnosis. However, obtaining such images usually takes more time and economic costs, which makes it impractical in most scenarios. In this paper, we first propose a novel Self-texture Transfer Super-resolution and Refocusing Network (STSRNet) to reconstruct HR multi-focal plane (MFP) images from a single 2D low-resolution (LR) wide field image without relying on scanning or any special devices. The proposed STSRNet consists of three parts: the backbone module for extracting features, the self-texture transfer module for transferring and fusing features, and the flexible reconstruction module for SR and refocusing. Specifically, the self-texture transfer module is designed for images with self-similarity such as cytological images and it searches for similar textures within the image and transfers to help MFP reconstruction. As for reconstruction module, it is composed of multiple pluggable components, each of which is responsible for a specific focal plane, so as to performs SR and refocusing all focal planes at one time to reduce computation. We conduct extensive experiments on cytological images and the experiments show that MFP images reconstructed by STSRNet have richer details in the axial and horizontal directions than input images. At the same time, the reconstructed MFP images also perform better than single 2D wide field images on high-level tasks. The proposed method provides relatively high-quality MFP images when real MFP images cannot be obtained, which greatly expands the application potential of LR wide-field images. To further promote the development of this field, we released our cytology dataset named RSDC for more researchers to use.
Keyword:
Feature extraction
Image reconstruction
Three-dimensional displays
Microscopy
Superresolution
Fuses
Biomedical imaging
Super resolution
multi-focal plane images
image enhancement
virtual refocusing
cytology
期刊
IF:
9.8
论文数:
6.2K
被引数:
3.7W
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