Parallel lensless compressive imaging via deep convolutional neural networks

We report a parallel lensless compressive imaging system, which enjoys real-time reconstruction using deep convolutional neural networks. A prototype composed of a low-cost LCD, 16 photo-diodes and isolation chambers, has been built. Each of these 16 channels captures a fraction of the scene with 16x16 pixels and they are performing in parallel. An efficient inversion algorithm based on deep convolutional neural networks is developed to reconstruct the image. We have demonstrated encouraging results using only 2% (relative to pixel numbers, e.g. 5 for a block with 16x16 pixels) measurements per sensor for digits and around 10% measurements per sensor for facial images. (c) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.