Content-Aware Depth-Adaptive Image Restoration
Tom Richard Vargis Siavash Ghiasvand
This work prioritizes building a modular pipeline that utilizes existing
models to systematically restore images, rather than creating new restoration
models from scratch. Restoration is carried out at an object-specific level,
with each object regenerated using its corresponding class label information.
The approach stands out by providing complete user control over the entire
restoration process. Users can select models for specialized restoration steps,
customize the sequence of steps to meet their needs, and refine the resulting
regenerated image with depth awareness. The research provides two distinct
pathways for implementing image regeneration, allowing for a comparison of
their respective strengths and limitations. The most compelling aspect of this
versatile system is its adaptability. This adaptability enables users to target
particular object categories, including medical images, by providing models
that are trained on those object classes.