SYNERGIZE VISION, LEARNING, AND OPTICS FOR VISUAL SENSING

About Us. We are a research group at the Elmore Family School of Electrical and Computer Engineering at Purdue University. We invent next-generation visual sensors that could revolutionize humans' capability to see the world. In particular, we build visual sensing systems that are unprecedentedly accurate, small, or power-efficient through the joint design of optics, electronics, and signal processing computation. We welcome collaborations! Feel free to connect using either of the methods listed below. If you are interested in joining the group as a PhD student or research assistant, please read this page before contacting us. 
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Featured Projects

Blurry Edges (CVPR 2025)

Have you ever wondered during a night how far that dark building is away from you?

We present a method that can robustly determine the object distances from a pair of noisy images with different depth-of-fields. It analyzes the blurriness of the image boundaries to predict the object distance using depth from defocus. 

The method outperforms previous depth from defocus algorithms, both learning and physics-based. It robustly generates depth maps on images with 4x more noise than typically used in previous methods. [Read More]

Focal Split (CVPR 2025)

Do you want a 3D camera that is portable, robust to motion, and does not emit light? 

Here is Focal Split,  a handheld, snapshot, passive depth camera with fully onboard power and computing (4.9W and 500 FLOPs per pixel). It is based on a custom optical system that mimics the eyes of jumping spiders, and the data is solely processed by a Raspberry PI. A DIY guide costing $500 is provided. [More information to come]

MetaHDR (2024). A snapshot HDR imaging and sensing system with a multifunctional metasurface. The work was featured by Spotlight on Optics! [Read More

Depth from Coupled Optical Differentiation (2024). A passive lighting 3D sensing system that estimates a depth map with only 36 floating point operations per pixel, 10x lower computational cost than the previous most efficient depth from defocus algorithms. [Read More]

  

CT-Bound (2024). A robust boundary detection algorithm for extremely noisy images. [Read More]

We acknowledge the following generous sponsors and partners of our research: