5 papers
Bridging Degradation Discrimination and Generation for Universal Image Restoration
JiaKui Hu, Zhengjian Yao, Lujia Jin +1
Universal image restoration is a critical task in low-level vision, requiring the model to remove various degradations from low-quality images to produce clean images with rich det…
Universal Image Restoration Pre-training via Masked Degradation Classification
JiaKui Hu, Zhengjian Yao, Lujia Jin +2
This study introduces a Masked Degradation Classification Pre-Training method (MaskDCPT), designed to facilitate the classification of degradation types in input images, leading to…
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance
JiaKui Hu, Zhengjian Yao, Lujia Jin +2
Translation equivariance is a fundamental inductive bias in image restoration, ensuring that translated inputs produce translated outputs. Attention mechanisms in modern restoratio…
Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training
Shuang Zeng, Lei Zhu, Xinliang Zhang +10
Medical image segmentation is a fundamental yet challenging task due to the arduous process of acquiring large volumes of high-quality labeled data from experts. Contrastive learni…
Universal Image Restoration Pre-training via Degradation Classification
JiaKui Hu, Lujia Jin, Zhengjian Yao +1
This paper proposes the Degradation Classification Pre-Training (DCPT), which enables models to learn how to classify the degradation type of input images for universal image resto…