Multi-task Image Restoration Guided By Robust DINO Features

• URL: http://arxiv.org/abs/2312.01677v3
• Date: Fri, 16 Aug 2024 14:53:09 GMT
• Title: Multi-task Image Restoration Guided By Robust DINO Features
• Authors: Xin Lin, Jingtong Yue, Kelvin C. K. Chan, Lu Qi, Chao Ren, Jinshan Pan, Ming-Hsuan Yang,
• Abstract summary: We propose mboxtextbfDINO-IR, a multi-task image restoration approach leveraging robust features extracted from DINOv2. We first propose a pixel-semantic fusion (PSF) module to dynamically fuse DINOV2's shallow features. By formulating these modules into a unified deep model, we propose a DINO perception contrastive loss to constrain the model training.
• Score: 88.74005987908443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-task image restoration has gained significant interest due to its inherent versatility and efficiency compared to its single-task counterpart. However, performance decline is observed with an increase in the number of tasks, primarily attributed to the restoration model's challenge in handling different tasks with distinct natures at the same time. Thus, a perspective emerged aiming to explore the degradation-insensitive semantic commonalities among different degradation tasks. In this paper, we observe that the features of DINOv2 can effectively model semantic information and are independent of degradation factors. Motivated by this observation, we propose \mbox{\textbf{DINO-IR}}, a multi-task image restoration approach leveraging robust features extracted from DINOv2 to solve multi-task image restoration simultaneously. We first propose a pixel-semantic fusion (PSF) module to dynamically fuse DINOV2's shallow features containing pixel-level information and deep features containing degradation-independent semantic information. To guide the restoration model with the features of DINOv2, we develop a DINO-Restore adaption and fusion module to adjust the channel of fused features from PSF and then integrate them with the features from the restoration model. By formulating these modules into a unified deep model, we propose a DINO perception contrastive loss to constrain the model training. Extensive experimental results demonstrate that our DINO-IR performs favorably against existing multi-task image restoration approaches in various tasks by a large margin. The source codes and trained models will be made available.

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