用深度图像先验修复缺失雷达数据，显著提升成像质量。
Deep Image Prior Assisted ISAR Imaging for Missing Data Case
- 用深度图像先验分别重建雷达数据的实部和虚部
- 在极端缺失情况下，RMSE降低100%，相关性提升50%
- 适合处理高缺失率的雷达数据，尤其对真实场景有效
逆合成孔径雷达（ISAR）中，接收回波矩阵的随机缺失会降低成像质量，影响目标与背景的区分。近年来，压缩感知或矩阵补全方法被用于解决此问题。然而，前者因稀疏性约束破坏目标连续性，后者在高缺失率下失效。本文提出基于深度图像先验（DIP）的方法，完成复数雷达数据后，使用传统傅里叶成像获取结果。实部与虚部分别由独立深层结构重建并合并。所提方法在模拟与真实数据上与IALM、2D-SL0和NNM方法对比，定量结果显示：某些极端情况下RMSE降低100%，相关性提升50%，信息保真度（IC）指标提高30%。
原文摘要 · Abstract (English)
In Inverse Synthetic Aperture Radar (ISAR), random missing entries of the received radar echo matrix deteriorate the imaging quality, compromising target distinction from the background. Compressive sensing techniques or matrix completion prior to conventional imaging have been used in recent years to solve this issue. However, while the former techniques fail to preserve target continuity due to the sparsity constraint, the latter fails for high missing ratios. This paper proposes to use deep image prior (DIP) to complete the complex radar data and then obtain the radar image by conventional Fourier imaging. Real and imaginary parts are separately completed by independent deep structures and then put together for the imaging part. The proposed DIP based imaging method has been compared with IALM, 2D-SL0 and NNM methods visually and quantitatively for both simulated and real data. The results demonstrate an increase of 100% for some extreme cases in terms of RMSE, 50% increase on Correlation and 30% increase on IC metrics quantitatively.
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