arXiv Computer Vision
6d ago

SR-Ground: Image Quality Grounding for Super-Resolved Content

SR‑Ground is a large‑scale dataset created to enable fine‑grained segmentation of visual artifacts in super‑resolved images. It contains 63,000 images processed by various state‑of‑the‑art SR models, each annotated at the pixel level for six distinct artifact types, validated through a crowdsourcing study with 1,062 participants. The dataset improves the training of image quality assessment models with grounding capabilities and supports a fine‑tuning pipeline that reduces perceptible artifacts in SR outputs, outperforming no‑reference methods on both benchmark and real‑world low‑resolution datasets.

By Artem Borisov, Evgeney Bogatyrev, Khaled Abud, Dmitriy Vatolin
arXiv Computer Vision
Sep 28

PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution

arXiv:2609.30988v1 Announce Type: new Abstract: Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while...

By Xin Di, Mingyu Shi, Yuanfei Bao, Long Peng, Yue Zhao, Jiaming Guo, Renjing Pei, Xueyang Fu, Yang Cao, Zheng-Jun Zha