4.7 Article

Fast Resolution Enhancement for Real Beam Mapping Using the Parallel Iterative Deconvolution Method

期刊

REMOTE SENSING
卷 15, 期 4, 页码 -

出版社

MDPI
DOI: 10.3390/rs15041164

关键词

real beam mapping; super-resolution; improved Poisson distribution-based maximum likelihood; GPU; parallel computing

向作者/读者索取更多资源

This paper introduces an improved Poisson distribution-based maximum likelihood (IPML) method that effectively improves the algorithm convergence speed without high-dimensional matrix operations. A GPU-based parallel processing architecture is proposed, along with a cooperative CPU-GPU working model, achieving parallel optimization of echo reception, preprocessing, and super-resolution processing. The proposed method significantly improves computational efficiency without sacrificing performance, as verified using real dataset.
Super-resolution methods for real beam mapping (RBM) imagery play a significant role in many microwave remote sensing applications. However, the existing super-resolution methods require high-dimensional matrix operations in the case of wide-field imaging, which makes it difficult to satisfy the requirements of real-time signal processing. To solve this problem, this paper introduces an improved Poisson distribution-based maximum likelihood (IPML) method by adding an adaptive iterative acceleration factor to effectively improve the algorithm convergence speed without introducing high-dimensional matrix operations. Furthermore, a GPU-based parallel processing architecture is proposed through the multithreading characteristics of the computing platform, and a cooperative CPU-GPU working model is constructed. This can realize the parallel optimization of the echo reception, preprocessing, and super-resolution processing. We verify that the proposed parallel super-resolution method can significantly improve the computational efficiency without sacrificing performance, using a real dataset.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据