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. this https URL

基于本地感知的 2D 领域的 Fieldscale 技术提供了一种自适应确定每个区域像素增益，并产生具有最小信息损失和高可见性的一致性空间一致的 8 位缩放图像，从而大大提高了图像质量评估和其他下游任务的性能。