63 citations · 87 across the 7 of their papers we have counts for
7 papers
End-to-end differentiable design of geometric waveguide displays
Xinge Yang, Zhaocheng Liu, Zhaoyu Nie +4
Geometric waveguides are a promising architecture for optical see-through augmented reality displays, but their performance is severely bottlenecked by the difficulty of jointly op…
Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur
Xinge Yang, Chuong Nguyen, Wenbin Wang +3
Modern cameras with large apertures often suffer from a shallow depth of field, resulting in blurry images of objects outside the focal plane. This limitation is particularly probl…
Tolerance-Aware Deep Optics
Jun Dai, Liqun Chen, Xinge Yang +3
Deep optics has emerged as a promising approach by co-designing optical elements with deep learning algorithms. However, current research typically overlooks the analysis and optim…
End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model
Xinge Yang, Matheus Souza, Kunyi Wang +3
Hybrid refractive-diffractive lenses combine the light efficiency of refractive lenses with the information encoding power of diffractive optical elements (DOE), showing great pote…
Task-Driven Lens Design
Xinge Yang, Qiang Fu, Yunfeng Nie +1
Classical lens design minimizes optical aberrations to produce sharp images, but is typically decoupled from downstream computer vision tasks. Existing end-to-end optical design le…
Aberration-Aware Depth-from-Focus
Xinge Yang, Qiang Fu, Mohammed Elhoseiny +1
Computer vision methods for depth estimation usually use simple camera models with idealized optics. For modern machine learning approaches, this creates an issue when attempting t…
Curriculum Learning for ab initio Deep Learned Refractive Optics
Xinge Yang, Qiang Fu, Wolfgang Heidrich
Deep optical optimization has recently emerged as a new paradigm for designing computational imaging systems using only the output image as the objective. However, it has been limi…