Semantic Segmentation Using Super Resolution Technique as Pre-Processing
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Abstract (arXiv)
Combining high-level and low-level visual tasks is a common technique in the field of computer vision. This work integrates the technique of image super resolution to semantic segmentation for document image binarization. It demonstrates that using image super-resolution as a preprocessing step can effectively enhance the results and performance of semantic segmentation.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 9 references that are in the map. First posted on arXiv 27 Jun 2023.
Builds on
4 of 9 references.
References · 4
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
- 2022DocEnTr: An End-to-End Document Image Enhancement Transformercited by 28
- 2023Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategycited by 9
Built on by
Citing papers per year: 2024: 1.
1 paper.