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Title:

Language-Driven Image Restoration and Semantic-Aware Quality Assessment: A Survey

Document type:
Zeitschriftenaufsatz
Author(s):
Liu, Mingyu; Shu, Haozhan; Cui, Yuning; Zhou, Xingcheng; Cao, Hu; Ren, Wenqi; Shi, Boxin; Knoll, Alois C
Year:
2026
Fulltext / DOI:
doi:10.20944/preprints202603.2366.v1
WWW:
https://www.preprints.org/frontend/manuscript/ff0d6c82513b93563ba21f101076f966/download_pub
Notes:
Image restoration aims to recover a high-quality image from its degraded counterpart by mitigating distortions introduced during acquisition, transmission, or environmental interaction. Despite the remarkable progress of deep learning–based restoration models, most conventional approaches remain tightly coupled to predefined degradation assumptions and pixel-level supervision, limiting their capability to handle complex and diverse scenarios or user-dependent restoration targets. Recent advan...
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