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arXiv · 2401.04680

CoordGate: Efficiently Computing Spatially-Varying Convolutions in Convolutional Neural Networks

Sunny Howard·Peter Norreys·Andreas Döpp
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Abstract

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.

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Sunny Howard, Peter Norreys, Andreas Döpp. 2024-01-09. CoordGate: Efficiently Computing Spatially-Varying Convolutions in Convolutional Neural Networks. https://arxiv.org/abs/2401.04680

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