Content-Aware Depth-Adaptive Image Restoration

The work prioritizes the development of a modular pipeline that efficiently utilizes existing models for image restoration instead of creating new ones from scratch. Restoration is conducted at an object-specific level, with each object being regenerated based on its corresponding class label information. What sets this approach apart is its provision of comprehensive user control throughout the restoration process. Users have the flexibility to choose models for specific restoration tasks, customize the sequence of steps according to their requirements, and enhance the resulting regenerated image with depth awareness. The study offers two distinct pathways for implementing image regeneration, allowing for a comparative analysis of their strengths and limitations. One of the main advantages of this adaptable system is its flexibility. Users can tailor the restoration process to target specific object categories, such as medical images, by leveraging models trained on those specific object classes. The entire code to replicate the pipeline is publicly accessible on [1].