Zhai Yu, Lei Jieyu, Xia Wenze, Han Shaokun, Liu Fei, Li Wenhao
Beijing Key Lab for Precision Optoelectronic Measurement Instrument and Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2020 Sep 11;20(18):5185. doi: 10.3390/s20185185.
This work introduces a super-resolution (SR) algorithm for range images on the basis of self-guided joint filtering (SGJF), adding the range information of the range image as a coefficient of the filter to reduce the influence of the intensity image texture on the super-resolved image. A range image SR recognition system is constructed to study the effect of four SR algorithms including the SGJF algorithm on the recognition of the laser radar (ladar) range image. The effects of different model library sizes, SR algorithms, SR factors and noise conditions on the recognition are tested via experiments. Results demonstrate that all tested SR algorithms can improve the recognition rate of low-resolution (low-res) range images to varying degrees and the proposed SGJF algorithm has a very good comprehensive recognition performance. Finally, suggestions for the use of SR algorithms in actual scene recognition are proposed on the basis of the experimental results.
这项工作基于自引导联合滤波（SGJF）引入了一种用于距离图像的超分辨率（SR）算法，将距离图像的距离信息作为滤波器的系数，以减少强度图像纹理对超分辨率图像的影响。构建了一个距离图像SR识别系统，以研究包括SGJF算法在内的四种SR算法对激光雷达（ladar）距离图像识别的影响。通过实验测试了不同模型库大小、SR算法、SR因子和噪声条件对识别的影响。结果表明，所有测试的SR算法都能不同程度地提高低分辨率（低分辨率）距离图像的识别率，并且所提出的SGJF算法具有非常好的综合识别性能。最后，根据实验结果对SR算法在实际场景识别中的应用提出了建议。
