Learned reconstructions for practical mask-based lensless imaging

08/30/2019 ∙ by Kristina Monakhova, et al. ∙ 3

Mask-based lensless imagers are smaller and lighter than traditional lensed cameras. In these imagers, the sensor does not directly record an image of the scene; rather, a computational algorithm reconstructs it. Typically, mask-based lensless imagers use a model-based reconstruction approach that suffers from long compute times and a heavy reliance on both system calibration and heuristically chosen denoisers. In this work, we address these limitations using a bounded-compute, trainable neural network to reconstruct the image. We leverage our knowledge of the physical system by unrolling a traditional model-based optimization algorithm, whose parameters we optimize using experimentally gathered ground-truth data. Optionally, images produced by the unrolled network are then fed into a jointly-trained denoiser. As compared to traditional methods, our architecture achieves better perceptual image quality and runs 20x faster, enabling interactive previewing of the scene. We explore a spectrum between model-based and deep learning methods, showing the benefits of using an intermediate approach. Finally, we test our network on images taken in the wild with a prototype mask-based camera, demonstrating that our network generalizes to natural images.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 9

page 12

Code Repositories

LenslessLearning

Code for Lensless Learning Paper


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.