arXiv Computer Vision By Lanqing Guo, Xijun Wang, Minchul Kim, Yu Yuan, Wes Robbins, Xingguang Zhang, Nicholas Chimitt, Stanley H. Chan, Zhangyang Wang, Xiaoming Liu

Restore What Matters: Lessons from Joint Restoration and Recognition

Read the original on arXiv Computer Vision →

The Flow has not summarised this story yet — read it at arXiv Computer Vision.

arXiv Computer Vision
Sep 11

UniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

UniH$^3$ is a new framework for all-in-one medical image restoration that unifies hierarchical homogeneity and heterogeneity. It introduces a Hierarchical Homogeneity Memory (H2M) module to distill and retrieve shared anatomical priors, and a Hierarchical Heterogeneity Balancer (H2B) to mitigate inter- and intra-task conflicts during training. Experiments on MedIR-2D-500K and MedIR-3D-3K show that UniH$^3$ achieves state‑of‑the‑art performance for both multi‑task and single‑task restoration.

By Zhiwen Yang, Jiayin Li, Chengyu Liu, Hui Zhang, Bingzheng Wei, Yan Xu
Hugging Face Trending Papers
Jun 3

Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction

Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question.