arXiv Computer Vision October 12, 2026 By Liuqian Wang, Hanyu Xie, Rongzhen Zhao, Qi Ming, Chunjie Ma, Wentao Chao, Yufei Gao, Chengming Liu, Lei Shi

MOSAIC: Multi-Scale Object-Centric Slot Adaptation with Image-Guided Prototype Conditioning for Remote Sensing Image Super-Resolution

Read the original on arXiv Computer Vision →

MOSAIC is an object‑centric framework that adapts pretrained diffusion Transformers for image restoration by learning compact structural conditions. It organizes degraded images into structural slots capturing object‑level and layout‑level evidence, integrates these across resolutions, and retrieves recurring priors from a shared prototype memory. Injecting these representations into selected layers of a frozen diffusion Transformer via lightweight residual modulation preserves the pretrained generative prior, yielding improved remote sensing image super‑resolution performance on UCMerced, AID, and RSSCN7 datasets.

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