1 research outputs found
RCRN: Real-world Character Image Restoration Network via Skeleton Extraction
Constructing high-quality character image datasets is challenging because
real-world images are often affected by image degradation. There are
limitations when applying current image restoration methods to such real-world
character images, since (i) the categories of noise in character images are
different from those in general images; (ii) real-world character images
usually contain more complex image degradation, e.g., mixed noise at different
noise levels. To address these problems, we propose a real-world character
restoration network (RCRN) to effectively restore degraded character images,
where character skeleton information and scale-ensemble feature extraction are
utilized to obtain better restoration performance. The proposed method consists
of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet
aims to preserve the structural consistency of the character and normalize
complex noise. Then, CiRNet reconstructs clean images from degraded character
images and their skeletons. Due to the lack of benchmarks for real-world
character image restoration, we constructed a dataset containing 1,606
character images with real-world degradation to evaluate the validity of the
proposed method. The experimental results demonstrate that RCRN outperforms
state-of-the-art methods quantitatively and qualitatively.Comment: Accepted to ACM MM 202