论文标题
热红外图像通过边缘感知指导插入
Thermal Infrared Image Inpainting via Edge-Aware Guidance
论文作者
论文摘要
图像介绍通过深度学习取得了基本进步。但是，几乎所有现有的涂漆方法旨在处理自然图像，而几乎没有具有广泛应用的目标热红外（TIR）图像。当应用于TIR图像时，常规的涂上方法通常会产生变形或模糊的内容。在本文中，我们提出了一项新的任务 - 热红外图像介绍，该图像旨在重建TIR图像的缺失区域。至关重要的是，我们提出了一种新型的基于深度学习的模型TIR填充。我们采用边缘发电机来完成破碎的TIR图像的精美边缘。完整的边缘被预测到标准化权重和偏见，以提高模型的边缘意识。此外，采用基于门控卷积的改进网络来提高TIR图像一致性。该实验表明，我们的方法优于Flir热数据集上的最先进的图像介入方法。
Image inpainting has achieved fundamental advances with deep learning. However, almost all existing inpainting methods aim to process natural images, while few target Thermal Infrared (TIR) images, which have widespread applications. When applied to TIR images, conventional inpainting methods usually generate distorted or blurry content. In this paper, we propose a novel task -- Thermal Infrared Image Inpainting, which aims to reconstruct missing regions of TIR images. Crucially, we propose a novel deep-learning-based model TIR-Fill. We adopt the edge generator to complete the canny edges of broken TIR images. The completed edges are projected to the normalization weights and biases to enhance edge awareness of the model. In addition, a refinement network based on gated convolution is employed to improve TIR image consistency. The experiments demonstrate that our method outperforms state-of-the-art image inpainting approaches on FLIR thermal dataset.
