50 research outputs found
CoordGate: efficiently computing spatially-varying convolutions in convolutional neural networks
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 existing approaches, offering a more robust and spatially
aware solution for CNNs in various computer vision applications