GAMA-IR: Global Additive Multidimensional Averaging for Fast Image
  Restoration
Youssef Mansour Reinhard Heckel
Deep learning-based methods have shown remarkable success for various image
restoration tasks such as denoising and deblurring. The current
state-of-the-art networks are relatively deep and utilize (variants of) self
attention mechanisms. Those networks are significantly slower than shallow
convolutional networks, which however perform worse. In this paper, we
introduce an image restoration network that is both fast and yields excellent
image quality. The network is designed to minimize the latency and memory
consumption when executed on a standard GPU, while maintaining state-of-the-art
performance. The network is a simple shallow network with an efficient block
that implements global additive multidimensional averaging operations. This
block can capture global information and enable a large receptive field even
when used in shallow networks with minimal computational overhead. Through
extensive experiments and evaluations on diverse tasks, we demonstrate that our
network achieves comparable or even superior results to existing
state-of-the-art image restoration networks with less latency. For instance, we
exceed the state-of-the-art result on real-world SIDD denoising by 0.11dB,
while being 2 to 10 times faster.