3 papers
cs.CV2026
UniRestorer++: Scalable Universal Image Restoration with Multi-Granularity Degradation Modeling
Jingbo Lin, Zhilu Zhang, Fan Li +5
Universal image restoration aims to handle diverse degradations within a single model, but a reliable scaling strategy remains underexplored. Increasing backbone depth and width ca…
cs.CV2024
UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity
Jingbo Lin, Zhilu Zhang, Wenbo Li +4
Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are…
cs.CV2023
Improving Image Restoration through Removing Degradations in Textual Representations
Jingbo Lin, Zhilu Zhang, Yuxiang Wei +3
In this paper, we introduce a new perspective for improving image restoration by removing degradation in the textual representations of a given degraded image. Intuitively, restora…