A Method For Eliminating Contour Errors In Self-Encoder Reconstructed Images
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Abstract (arXiv)
In this paper, we propose a self-supervised twin network approach based on this a priori. The method of generating the approximate10 edge information of an image and then differentially eliminating the edge errors11 in the reconstructed image with a dilate algorithm. This is used to improve the12 accuracy of the reconstructed image and to separate foreign matter and noise from13 the original image, so that it can be visualized in a more practical scene
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References · 5
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
- 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
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