论文标题
高保真图像转换的频谱一致的UNET
Spectrally Consistent UNet for High Fidelity Image Transformations
论文作者
论文摘要
卷积神经网络（CNN）是当前用于许多成像任务的事实模型，因为它们的高学习能力及其建筑质量。无处不在的UNET体系结构提供了一种有效且多规模的解决方案，结合了本地和全局信息。尽管UNET架构取得了成功，但使用上采样层的使用可能会导致人工制品。在这项工作中，提出了一种评估UNET的结构偏差以及对产出的影响的方法，表征了它们对傅立叶域中的影响。根据引导图像滤波器的新颖使用，提出了一个新的UP采样模块，该模块在UNET体系结构中使用时提供了频谱一致的输出，从而形成了引导的UNET（Gunet）。应用和评估了Gunet架构，例如，灰色尺度图像的逆音映射/动态范围扩展和色彩的应用，并显示出可提供更高的保真度输出。
Convolutional Neural Networks (CNNs) are the current de-facto models used for many imaging tasks due to their high learning capacity as well as their architectural qualities. The ubiquitous UNet architecture provides an efficient and multi-scale solution that combines local and global information. Despite the success of UNet architectures, the use of upsampling layers can cause artefacts. In this work, a method for assessing the structural biases of UNets and the effects these have on the outputs is presented, characterising their impact in the Fourier domain. A new upsampling module is proposed, based on a novel use of the Guided Image Filter, that provides spectrally consistent outputs when used in a UNet architecture, forming the Guided UNet (GUNet). The GUNet architecture is applied and evaluated for example applications of inverse tone mapping/dynamic range expansion and colourisation from grey-scale images and is shown to provide higher fidelity outputs.
