Generative models provide a powerful framework for tackling various imaging tasks, from image enhancement and inpainting to style transfer. A central challenge lies in preserving fine details while maintaining high-fidelity realism, pushing the boundaries of generative models beyond conventional synthesis.
Restoring and enhancing degraded visual data requires robust low-level vision models. Research efforts address tasks such as denoising, deblurring, and super-resolution, with a particular focus on handling challenging conditions like extreme low-light environments and motion blur.
Deploying high-quality vision models in mobile and embedded environments demands efficiency. This research direction explores lightweight network architectures and hardware-aware algorithms, optimizing computational cost while preserving visual quality.
Accurate 3D reconstruction is essential for a wide range of applications, from AR/VR to robotics. Research in this area focuses on leveraging mobile cameras and depth sensors for high-precision scene reconstruction, as well as developing efficient SLAM and spatial recovery techniques for both indoor and outdoor settings.