  Cameras digitize real-world scenes as pixel intensity values with a limited
value range given by the available bits per pixel (bpp). High Dynamic Range
(HDR) cameras capture those luminance values in higher resolution through an
increase in the number of bpp. Most displays, however, are limited to 8 bpp.
Naive HDR compression methods lead to a loss of the rich information contained
in those HDR images. In this paper, tone mapping algorithms for thermal
infrared images with 16 bpp are investigated that can preserve this
information. An optimized multi-scale Retinex algorithm sets the baseline. This
algorithm is then approximated with a deep learning approach based on the
popular U-Net architecture. The remaining noise in the images after tone
mapping is reduced implicitly by utilizing a self-supervised deep learning
approach that can be jointly trained with the tone mapping approach in a
multi-task learning scheme. Further discussions are provided on denoising and
deflickering for thermal infrared video enhancement in the context of tone
mapping. Extensive experiments on the public FLIR ADAS Dataset prove the
effectiveness of our proposed method in comparison with the state-of-the-art.
