Unrolling ADMM with learned parameters reconstructs DiffuserCam lensless images 20x faster and with better LPIPS perceptual similarity than classical ADMM.
Video from Stills: Lensless Imaging with Rolling Shutter
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Because image sensor chips have a finite bandwidth with which to read out pixels, 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.
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Learned reconstructions for practical mask-based lensless imaging
Unrolling ADMM with learned parameters reconstructs DiffuserCam lensless images 20x faster and with better LPIPS perceptual similarity than classical ADMM.