Visual signals are inherently embedded within low-dimensional manifolds of high-dimensional data spaces. Exploring the distribution of high-quality visual signals and constructing robust priors is not only a fundamental challenge in computer vision but also a shared theoretical pillar for the following research areas:

Our lab focuses on the theory of visual prior modeling and its cutting-edge applications in enhancement, compression, and generation.