  Classical convolutional neural networks (cCNNs) are very good at categorizing
objects in images. But, unlike human vision which is relatively robust to noise
in images, the performance of cCNNs declines quickly as image quality worsens.
Here we propose to use recurrent connections within the convolutional layers to
make networks robust against pixel noise such as could arise from imaging at
low light levels, and thereby significantly increase their performance when
tested with simulated noisy video sequences. We show that cCNNs classify images
with high signal to noise ratios (SNRs) well, but are easily outperformed when
tested with low SNR images (high noise levels) by convolutional neural networks
that have recurrency added to convolutional layers, henceforth referred to as
gruCNNs. Addition of Bayes-optimal temporal integration to allow the cCNN to
integrate multiple image frames still does not match gruCNN performance.
Additionally, we show that at low SNRs, the probabilities predicted by the
gruCNN (after calibration) have higher confidence than those predicted by the
cCNN. We propose to consider recurrent connections in the early stages of
neural networks as a solution to computer vision under imperfect lighting
conditions and noisy environments; challenges faced during real-time video
streams of autonomous driving at night, during rain or snow, and other
non-ideal situations.
