| 严伟,杨韬,吴志祥,刘岩,胡淑姬,王春勇,来建成,李振华.激光雷达稀疏图像的残差通道注意力机制复原重建方法研究[J].电子测量与仪器学报,2024,38(12):35-42 |
| 激光雷达稀疏图像的残差通道注意力机制复原重建方法研究 |
| Research on sparse image restoration and reconstruction method ofLiDAR based on residual channel attention block |
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| DOI： |
| 中文关键词: 激光雷达 图像复原 稀疏图像 注意力机制 残差通道 |
| 英文关键词:LiDAR image restoration sparse image attention mechanism residual channel |
| 基金项目:国家自然科学基金（61971225, 62221004, 62175110）、江苏省卓越博士后计划（2024ZB370）项目资助 |
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| 摘要点击次数: 369 |
| 全文下载次数: 323 |
| 中文摘要: |
| 稀疏采样与图像复原相结合不但可以压缩数据容量，而且还可以提高成像速度，对于发展高分辨率激光雷达成像技术具有重要意义。为了改善稀疏采样图像的复原效果，本文设计了一种新的残差通道注意力机制网络块，并将残差通道注意力机制引入到基于压缩感知迭代软阈值方法的深度展开网络中，抑制图像复原重建中因缺失高频信息而导致的模糊现象，形成了一种新的激光雷达稀疏采样图像的复原重建方法。该方法结合了传统压缩感知重建方法和神经网络方法的优势，与传统压缩感知重建方法相比，具有更快的重建速度；与现有神经网络方法相比，增强了结构洞察力，改进了重建图像模糊问题。以Middlebury Stereo Data 2006为测试数据集的验证计算表明，本文提出的方法与SDA、ReconNet、TVAL3、D-AMP和IRCNN等方法相比不但具有更好的图像重建质量，而且具有较高的计算效率；当稀疏采样比率为25%时，复原后图像的峰值信噪比要比其他方法高1.6 dB以上，是一种综合性能较理想的激光雷达稀疏图像复原方法。 |
| 英文摘要: |
| The combination of sparse sampling and image restoration can not only compress data capacity, but also improve imaging speed, which is of great significance for the development of high-resolution LiDAR imaging technology. In order to improve the restoration effect of sparse sampled images, a new residual channel attention network block was designed in the paper, and the residual channel attention block was introduced into a deep unfolding network based on compressed sensing iterative soft threshold method to suppress the blurring phenomenon caused by the loss of high-frequency information in image restoration and reconstruction, forming a new method for the restoration and reconstruction of sparse sampled LiDAR images. This method combines the advantages of traditional compressed sensing reconstruction methods and neural network methods, and has a faster reconstruction speed compared to traditional compressed sensing reconstruction methods. Compared with existing neural network methods, it enhances structural insight and improves the problem of image blur in reconstruction. The validation calculations using Middlebury Stereo Data 2006 as the test dataset show that our method not only has better image reconstruction quality compared to SDA, ReconNet, TVAL3, D-AMP, and IRCNN methods, but also has higher computational efficiency; When the sparse sampling ratio is 25%, the peak signal-to-noise ratio (PSNR) of the restored image is more than 1.6 dB higher than other methods, making it an ideal method for restoring sparse LiDAR images with good overall performance. |
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