Fast and Accurate Reconstruction of Compressed Color Light Field
This work addresses the computational bottleneck in light field imaging for applications like photography and depth sensing, though it is incremental as it builds on prior compressed sensing methods.
The paper tackles the problem of slow decompression in compressed light field photography by proposing a computationally efficient neural network that reconstructs high-quality color light fields from a single coded image, outperforming existing methods in both recovery quality and speed.
Light field photography has been studied thoroughly in recent years. One of its drawbacks is the need for multi-lens in the imaging. To compensate that, compressed light field photography has been proposed to tackle the trade-offs between the spatial and angular resolutions. It obtains by only one lens, a compressed version of the regular multi-lens system. The acquisition system consists of a dedicated hardware followed by a decompression algorithm, which usually suffers from high computational time. In this work, we propose a computationally efficient neural network that recovers a high-quality color light field from a single coded image. Unlike previous works, we compress the color channels as well, removing the need for a CFA in the imaging system. Our approach outperforms existing solutions in terms of recovery quality and computational complexity. We propose also a neural network for depth map extraction based on the decompressed light field, which is trained in an unsupervised manner without the ground truth depth map.