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

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.

Automated summary

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.