ESTGN: Enhanced Self-Mined Text Guided Super-Resolution Network for Superior Image Super Resolution

In this paper, we propose a novel Enhanced Self-mined Text Guided Super-resolution Network (ESTGN) for single image super-resolution (SISR). Unlike preceding methods, ESTGN autonomously mines task-related text from images and uses it to guide SR for high-frequency detail restoration. The proposed me...

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Bibliographic Details
Published inICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3655 - 3659
Main Authors Li, Qipei, Ying, Zefeng, Pan, Da, Fan, Zhaoxin, Shi, Ping
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.04.2024
Subjects
Acoustics
Benchmark testing
Data mining
Image restoration
Image Super-resolution
Information retrieval
Modal Balance
Multi-modal Learning
Semantics
Superresolution
Text-mining
Vision-language Correspond
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Summary:In this paper, we propose a novel Enhanced Self-mined Text Guided Super-resolution Network (ESTGN) for single image super-resolution (SISR). Unlike preceding methods, ESTGN autonomously mines task-related text from images and uses it to guide SR for high-frequency detail restoration. The proposed methods include the Self-mined Text Information Extraction Module, Multi-resolution Text-aware Gradient Balance Module, and Masked Text-conditioned Attention Module. Our method can fully leverage self-mined textual semantic information and enhance gradient propagation in text. We validate our method with extensive experiments on the benchmark dataset, where ESTGN significantly outperforms the baseline model and sets a new state-of-the-art. This work opens up a promising avenue for the integration of text information in image SR tasks.
ISSN:2379-190X
DOI:10.1109/ICASSP48485.2024.10448088