Super-resolution for semantic segmentation

Xuan Zhang; Guoping Xu; Wentao Liao; Xinglong Wu

doi:10.1117/12.2643026

12 October 2022 Super-resolution for semantic segmentation

Xuan Zhang, Guoping Xu, Wentao Liao, Xinglong Wu

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421G (2022) https://doi.org/10.1117/12.2643026
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Image segmentation is a classical problem in the field of computer vision. With the extensive development of deep learning, it has achieved much progress in semantic segmentation. However, the mainstream networks used in deep learning such as Fast-SCNN, U-Net, which still face challenges in image segmentation. A common problem is that linear interpolation is used in the up-sampling stage of these networks to obtain high-resolution images. Due to the lack of sufficient feature information, the contours of the objects in the image are blurred and grided. For this reason, we propose a new super-resolution (SR) method to replace the up-sampling with linear interpolation in the network model. Five representative networks integrated with our proposed SR module are used for verification on the CamVid data set. The experimental results show that our method has a 2%~4% improvement in mIoU (the mean value of Intersection over Union) and a 2%~3% improvement in pixel accuracy, which demonstrates its generalization and effectiveness of our method.

Citation Download Citation

Xuan Zhang, Guoping Xu, Wentao Liao, and Xinglong Wu "Super-resolution for semantic segmentation", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421G (12 October 2022); https://doi.org/10.1117/12.2643026

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KEYWORDS

Image segmentation

Super resolution

Image processing

Convolution

Data modeling

Buildings

Image resolution

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