NIR image colorization with graph-convolutional neural networks

D. Valsesia, Giulia Fracastoro, E. Magli
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引用次数: 5

Abstract

Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations.
基于图卷积神经网络的近红外图像着色
近红外(NIR)图像的着色是一个具有挑战性的问题,因为在红外波长下材料的特性不同,从而降低了与可见光图像的相关性。在本文中,我们研究了图卷积神经网络如何以非局部自相似性的形式利用比标准cnn更强大的归纳偏差。它的影响是通过显示仅以均方误差作为损失的训练如何导致标准CNN的糟糕结果来评估的,而图卷积网络产生了更清晰、更逼真的着色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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