Lightless Fields: Enhancement and Denoising of Light-Deficient Light Fields

Modern focused light field cameras are capable of capturing video at over 160 frames per second, but in so doing sacrifice shutter speed. Outside of laboratory environments, lighting can be problematic resulting in noisy light fields and poor depth reconstruction. To enhance and denoise modern focused light field cameras, we create a unique deep neural network that allows for the full light field to be processed at once, eliminates stitching artifacts, and takes advantage of feature redundancy between neighboring microlenses. We show that our double U-Net network, ENH-W, significantly outperforms several popular architectures and light field denoising methods in both visual and depth metrics.