Colored glass, which is commonly seen in modern city life, often degrades images taken through it with co-occurring reflection and color bias due to its optical property of simultaneous transmission, reflection, and wavelength-selective absorption.~Recovering the clean background behind colored glass is inherently challenging due to the mutual interference of two degradations within a single mixture observation, and has barely been specifically considered by existing image restoration methods. In this paper, we aim at realizing faithful background scene recovery for an image taken in front of colored glass. We first analyze the formation model of mixed degradations caused by colored glass, and propose a cooperative framework to address the mutual interference problem, featuring a novel glass color invariant loss and progressive refinement. Besides, we propose a data synthesis strategy for network training. Experimental results on our newly collected real-world dataset show that our proposed method achieves state-of-the-art performance.
Scopus Subject Areas
- Signal Processing
- Media Technology
- Computer Science Applications
- Electrical and Electronic Engineering
- colored glass
- Image restoration