  Image restoration tasks traditionally rely on convolutional neural networks.
However, given the local nature of the convolutional operator, they struggle to
capture global information. The promise of attention mechanisms in Transformers
is to circumvent this problem, but it comes at the cost of intensive
computational overhead. Many recent studies in image restoration have focused
on solving the challenge of balancing performance and computational cost via
Transformer variants. In this paper, we present CascadedGaze Network (CGNet),
an encoder-decoder architecture that employs Global Context Extractor (GCE), a
novel and efficient way to capture global information for image restoration.
The GCE module leverages small kernels across convolutional layers to learn
global dependencies, without requiring self-attention. Extensive experimental
results show that our computationally efficient approach performs competitively
to a range of state-of-the-art methods on synthetic image denoising and single
image deblurring tasks, and pushes the performance boundary further on the real
image denoising task.
