论文标题
锥体边缘图和基于注意力的指导热超分辨率
Pyramidal Edge-maps and Attention based Guided Thermal Super-resolution
论文作者
论文摘要
由于图像之间的光谱范围差异，使用可见范围图像的热图像的指导超分辨率（GSR）具有挑战性。反过来，这意味着图像之间存在明显的纹理不匹配，这表现为超级分辨的热图像中的模糊和幽灵伪影。为了解决这个问题，我们基于从可见图像中提取的金字塔边缘映射提出了一种新颖的GSR算法。我们提出的网络有两个子网络。第一个子网络超级溶解了低分辨率的热图像，而第二个则以日益增长的感知量表从可见图像中获得边缘图，并在基于注意力的融合的帮助下将它们集成到超分辨率子网中。多级边缘的提取和集成使超分辨率网络可以逐步处理纹理到对象级别的信息，从而更直接地识别输入图像之间的重叠边缘。广泛的实验表明，我们的模型在定量和定性上都优于最先进的GSR方法。
Guided super-resolution (GSR) of thermal images using visible range images is challenging because of the difference in the spectral-range between the images. This in turn means that there is significant texture-mismatch between the images, which manifests as blur and ghosting artifacts in the super-resolved thermal image. To tackle this, we propose a novel algorithm for GSR based on pyramidal edge-maps extracted from the visible image. Our proposed network has two sub-networks. The first sub-network super-resolves the low-resolution thermal image while the second obtains edge-maps from the visible image at a growing perceptual scale and integrates them into the super-resolution sub-network with the help of attention-based fusion. Extraction and integration of multi-level edges allows the super-resolution network to process texture-to-object level information progressively, enabling more straightforward identification of overlapping edges between the input images. Extensive experiments show that our model outperforms the state-of-the-art GSR methods, both quantitatively and qualitatively.
