Zhou Yicheng, Liu Yuan, Yuan Liyin, Chen Qian, Gu Guohua, Sui Xiubao
Opt Express. 2023 Oct 23;31(22):36171-36187. doi: 10.1364/OE.496484.
Infrared image super-resolution technology aims to overcome the pixel size limitation of the infrared focal plane array for higher resolution images. Due to the real-world images with different resolutions having more complex degradation processes than mathematical calculation, most existing super-resolution methods using the synthetic data obtained by bicubic interpolation achieve unsatisfactory reconstruction performance in real-world scenes. To solve this, this paper innovatively proposes an infrared real-world dataset with different resolutions based on a refrigerated thermal detector and the infrared zoom lens, enabling the network to acquire more realistic details. We obtain images under different fields of view by adjusting the infrared zoom lens and then achieve the scale and luminance alignment of high and low-resolution (HR-LR) images. This dataset can be used for infrared image super-resolution, with an up-sampling scale of two. In order to learn complex features of infrared images efficiently, an asymmetric residual block structure is proposed to effectively reduce the number of parameters and improve the performance of the network. Finally, to solve the slight misalignment problem in the pre-processing stage, contextual loss and perceptual loss are introduced to improve the visual performance. Experiments show that our method achieves superior results both in reconstruction effect and practical value for single infrared image super-resolution in real scenarios.
红外图像超分辨率技术旨在克服红外焦平面阵列的像素尺寸限制，以获得更高分辨率的图像。由于具有不同分辨率的真实世界图像的退化过程比数学计算更为复杂，大多数现有的使用双三次插值获得的合成数据的超分辨率方法在真实场景中的重建性能并不理想。为了解决这个问题，本文创新性地提出了一个基于制冷热探测器和红外变焦镜头的具有不同分辨率的红外真实世界数据集，使网络能够获取更真实的细节。我们通过调整红外变焦镜头获得不同视场下的图像，然后实现高分辨率和低分辨率（HR-LR）图像的尺度和亮度对齐。该数据集可用于红外图像超分辨率，上采样尺度为2。为了有效地学习红外图像的复杂特征，提出了一种非对称残差块结构，以有效减少参数数量并提高网络性能。最后，为了解决预处理阶段的轻微失准问题，引入上下文损失和感知损失以提高视觉性能。实验表明，我们的方法在真实场景中的单红外图像超分辨率的重建效果和实用价值方面均取得了优异的结果。
