The Computational Imaging Systems Lab
UC San Diego
Current areas of interest
Learning Optical Designs
What is the ideal camera for capturing optical flow? What about classifying objects? Data-driven System Design is a foundational technology of next generation imaging system design that lets us answer questions like these. By joining differentiable hardware models and image processing, we use data to jointly optimize both optics and algorithms.
Optical Neural Imaging
Imaging fluorescence dynamics of neurons remains a tremendous challenge for optical imaging. Developing methods to improve the field of view, time resolution, depth capability, device size, and scattering resistance of imaging tools will aid neuroscientists in connecting neural dynamics to animal behavior.
Refining the designs of computational photography hardware (optics, sensors) may enable even better photography in the future. Our lab is exploring novel designs for HDR capture, high speed, and multi-spectral sensing with applications in computational photography and potentially autonomous vehicle vision systems.
Faster IR Spectroscopy
Optical infrared spectroscopic sensing entails measuring 3 spectral variables across 2 (or 3!) spatial dimensions. Developing efficient sampling and reconstruction algorithms will greatly increase the measurement speed of this high dimensional nonlinear optical problem
Monitoring the micro- and mesoscopic contents of the ocean requires sensing tiny objects across vast volumes of water. This is an exciting area for computational imaging techniques such as holography and high throughput imaging.
Differentiable renderers enable bringing the simulation efficiency developed in the graphics community to bear on realistic hardware simulation problems. This is an exciting area because it enables fast, accurate modeling of the outputs from optical systems, and can provide the necessary derivatives to drive end-to-end optimization.
This project demonstrates the innate compressive video properties of DiffuserCam. Because image sensor chips have a finite ADC bandwidth, recording video typically requires a trade-off between frame rate and pixel count. Compressed sensing techniques can circumvent this trade-off by assuming that the image is compressible. Here, we propose using multiplexing optics to spatially compress the scene, enabling information about the whole scene to be sampled from a row of sensor pixels, which can be read off quickly via a rolling shutter CMOS sensor. Conveniently, such multiplexing can be achieved with a simple lensless, diffuser-based imaging system. Using sparse recovery methods, we are able to recover 140 video frames at over 4,500 frames per second, all from a single captured image with a rolling shutter sensor. Our proof-of-concept system uses easily-fabricated diffusers paired with an off-the-shelf sensor. The resulting prototype enables compressive encoding of high frame rate video into a single rolling shutter exposure, and exceeds the sampling-limited performance of an equivalent global shutter system for sufficiently sparse objects. Best paper at ICCP 2019. [IEEE][ArXiV]