  How to aggregate spatial information plays an essential role in
learning-based image restoration. Most existing CNN-based networks adopt static
convolutional kernels to encode spatial information, which cannot aggregate
spatial information adaptively. Recent transformer-based architectures achieve
adaptive spatial aggregation. But they lack desirable inductive biases of
convolutions and require heavy computational costs. In this paper, we propose a
kernel basis attention (KBA) module, which introduces learnable kernel bases to
model representative image patterns for spatial information aggregation.
Different kernel bases are trained to model different local structures. At each
spatial location, they are linearly and adaptively fused by predicted
pixel-wise coefficients to obtain aggregation weights. Based on the KBA module,
we further design a multi-axis feature fusion (MFF) block to encode and fuse
channel-wise, spatial-invariant, and pixel-adaptive features for image
restoration. Our model, named kernel basis network (KBNet), achieves
state-of-the-art performances on more than ten benchmarks over image denoising,
deraining, and deblurring tasks while requiring less computational cost than
previous SOTA methods.
