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.

翻译：相机将真实场景数字化为像素强度值，其取值范围受限于每像素可用比特数。高动态范围相机通过增加每像素比特数，以更高分辨率捕获这些亮度值。然而，大多数显示器仅支持8bpp。简单的HDR压缩方法会导致这些HDR图像中丰富信息的丢失。本文针对16bpp热红外图像，研究能保留此类信息的色调映射算法。优化的多尺度Retinex算法作为基准方法，随后利用基于流行U-Net架构的深度学习方法对其进行近似。通过采用自监督深度学习方法，可在多任务学习框架中与色调映射方法联合训练，隐式降低色调映射后图像中的残留噪声。本文还进一步讨论了色调映射背景下热红外视频增强中的去噪与去闪烁问题。在公开FLIR ADAS数据集上的大量实验证明，与现有最先进方法相比，本文所提方法具有有效性。