  The convolution neural nets (conv nets) have achieved a state-of-the-art
performance in many applications of image and video processing. The most recent
studies illustrate that the conv nets are fragile in terms of recognition
accuracy to various image distortions such as noise, scaling, rotation, etc. In
this study we focus on the problem of robust recognition accuracy of random
noise distorted images. A common solution to this problem is either to add a
lot of noisy images into a training dataset, which can be very costly, or use
sophisticated loss function and denoising techniques. We introduce a novel conv
net architecture with multiple streams. Each stream is taking a certain
intensity slice of the original image as an input, and stream parameters are
trained independently. We call this novel network a "Streaming Net". Our
results indicate that Streaming Net outperforms 1-stream conv net (employed as
a single stream) and 1-stream wide conv net (employs the same number of filters
as Streaming Net) in recognition accuracy of noise-corrupted images, while
producing the same or higher recognition accuracy of no noise images in almost
all of the tests. Thus, we introduce a new simple method to increase robustness
of recognition of noisy images without using data generation or sophisticated
training techniques.
