  Recent progress on Transformers and multi-layer perceptron (MLP) models
provide new network architectural designs for computer vision tasks. Although
these models proved to be effective in many vision tasks such as image
recognition, there remain challenges in adapting them for low-level vision. The
inflexibility to support high-resolution images and limitations of local
attention are perhaps the main bottlenecks. In this work, we present a
multi-axis MLP based architecture called MAXIM, that can serve as an efficient
and flexible general-purpose vision backbone for image processing tasks. MAXIM
uses a UNet-shaped hierarchical structure and supports long-range interactions
enabled by spatially-gated MLPs. Specifically, MAXIM contains two MLP-based
building blocks: a multi-axis gated MLP that allows for efficient and scalable
spatial mixing of local and global visual cues, and a cross-gating block, an
alternative to cross-attention, which accounts for cross-feature conditioning.
Both these modules are exclusively based on MLPs, but also benefit from being
both global and `fully-convolutional', two properties that are desirable for
image processing. Our extensive experimental results show that the proposed
MAXIM model achieves state-of-the-art performance on more than ten benchmarks
across a range of image processing tasks, including denoising, deblurring,
deraining, dehazing, and enhancement while requiring fewer or comparable
numbers of parameters and FLOPs than competitive models. The source code and
trained models will be available at
\url{https://github.com/google-research/maxim}.
