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.

翻译：光学成像系统由于点扩散函数（PSF）的存在，其分辨率固有地受到限制，该函数对图像施加静态但空间变化的卷积。这种退化可通过卷积神经网络（CNN）特别是去模糊技术加以解决。然而，当前解决方案在高效计算空间变化卷积方面面临一定局限。本文提出CoordGate，一种新型轻量级模块，它利用乘法门控机制和坐标编码网络，在CNN中实现空间变化卷积的高效计算。CoordGate可根据滤波器的空间位置选择性放大或衰减其作用，实际上起到类似局部连接神经网络的效果。该模块在U-Net架构中的有效性通过图像去模糊这一挑战性问题得到验证。实验结果表明，CoordGate优于传统方法，为各类计算机视觉应用中的CNN提供了更鲁棒且具备空间感知能力的解决方案。