Thermal infrared (TIR) cameras are emerging as promising sensors in safety-related fields due to their robustness against external illumination. However, RAW TIR image has 14 bits of pixel depth and needs to be rescaled into 8 bits for general applications. Previous works utilize a global 1D look-up table to compute pixel-wise gain solely based on its intensity, which degrades image quality by failing to consider the local nature of the heat. We propose Fieldscale, a rescaling based on locality-aware 2D fields where both the intensity value and spatial context of each pixel within an image are embedded. It can adaptively determine the pixel gain for each region and produce spatially consistent 8-bit rescaled images with minimal information loss and high visibility. Consistent performance improvement on image quality assessment and two other downstream tasks support the effectiveness and usability of Fieldscale. All the codes are publicly opened to facilitate research advancements in this field. https://github.com/hyeonjaegil/fieldscale

翻译：热红外（TIR）相机因其对外部光照的鲁棒性，在安全相关领域正成为极具前景的传感器。然而，原始TIR图像具有14位像素深度，在通用应用中需重缩放至8位。先前工作采用全局一维查找表，仅基于像素强度计算像素增益，由于未能考虑热量的局部特性，导致图像质量下降。我们提出Fieldscale，一种基于局部感知二维场的重缩放方法，其中同时嵌入了图像内每个像素的强度值及其空间上下文信息。该方法能够自适应地确定每个区域的像素增益，生成空间一致的8位重缩放图像，实现最小的信息损失和良好的可视性。在图像质量评估及另外两项下游任务上取得的一致性能提升，验证了Fieldscale的有效性与实用性。所有代码均已公开，以促进该领域的研究进展。https://github.com/hyeonjaegil/fieldscale