CoordGate: Efficiently Computing Spatially-Varying Convolutions in
  Convolutional Neural Networks
Sunny Howard Peter Norreys Andreas Döpp
Optical imaging systems are inherently limited in their resolution due to the
point spread function (PSF), which applies a static, yet spatially-varying,
convolution to the image. This degradation can be addressed via Convolutional
Neural Networks (CNNs), particularly through deblurring techniques. However,
current solutions face certain limitations in efficiently computing
spatially-varying convolutions. In this paper we propose CoordGate, a novel
lightweight module that uses a multiplicative gate and a coordinate encoding
network to enable efficient computation of spatially-varying convolutions in
CNNs. CoordGate allows for selective amplification or attenuation of filters
based on their spatial position, effectively acting like a locally connected
neural network. The effectiveness of the CoordGate solution is demonstrated
within the context of U-Nets and applied to the challenging problem of image
deblurring. The experimental results show that CoordGate outperforms
conventional approaches, offering a more robust and spatially aware solution
for CNNs in various computer vision applications.