# Semantic Segmentation Using Super Resolution Technique as Pre-Processing

Source: [Semantic Segmentation Using Super Resolution Technique as Pre-Processing](<https://www.sota2.com/research/paper/68fa6fed1e209131cea48356>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:30:17.231Z

Authors: Chih-Chia Chen, Wei-Han Chen, Jen-Shiun Chiang, Chun-Tse Chien, Tingkai Chang

Published: 2023-06-27T05:43:16.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2306.15218>)

[PDF](<https://arxiv.org/pdf/2306.15218.pdf>)