22 research outputs found
"Zero-Shot" Super-Resolution using Deep Internal Learning
Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance
in the past few years. However, being supervised, these SR methods are
restricted to specific training data, where the acquisition of the
low-resolution (LR) images from their high-resolution (HR) counterparts is
predetermined (e.g., bicubic downscaling), without any distracting artifacts
(e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images,
however, rarely obey these restrictions, resulting in poor SR results by SotA
(State of the Art) methods. In this paper we introduce "Zero-Shot" SR, which
exploits the power of Deep Learning, but does not rely on prior training. We
exploit the internal recurrence of information inside a single image, and train
a small image-specific CNN at test time, on examples extracted solely from the
input image itself. As such, it can adapt itself to different settings per
image. This allows to perform SR of real old photos, noisy images, biological
data, and other images where the acquisition process is unknown or non-ideal.
On such images, our method outperforms SotA CNN-based SR methods, as well as
previous unsupervised SR methods. To the best of our knowledge, this is the
first unsupervised CNN-based SR method