QR分解-凸集投影协同超分辨率重建

doi:10.12305/j.issn.1001-506X.2026.05.03

Abstract

Abstract:

A super-resolution reconstruction method integrating physical priors and numerical optimization is proposed to address the sub-pixel displacement and oversampling information redundancy caused by target-platform relative motion in push-broom imaging. Firstly, the oversampling ratio and optimal super-resolution factor are derived based on target motion velocity and platform parameters. Secondly, stable solutions for ill-posed equations are achieved through regularized QR decomposition of regularized augmented matrices, suppressing noise amplification and artifact generation. Finally, spatiotemporal coupling constraints of imaging systems are embedded via the projection onto convex sets method, realizing detail enhancement and physically consistent reconstruction. Simulation experiments demonstrate that this method improves the local structural similarity index to 0.8402 in infrared remote sensing imagery, while reducing the relative root mean square error of spectral restoration by 80.17%, outperforming conventional generalized inverse and iterative interpolation algorithms. The total processing time for 41 spectral channels is better than 150 ms, demonstrating its engineering practical value in hyperspectral data processing.

Key words: linear push-broom oversampling, super-resolution reconstruction, regularized QR decomposition, projection onto convex sets （POCS） method

CLC Number:

TP 391.41

Junbo ZHAO, Hongxia MAO, Huijie ZHAO, Youkun ZHANG, Chang LIU. Synergistic super-resolution reconstruction via QR decomposition and POCS[J]. Systems Engineering and Electronics, 2026, 48(5): 1474-1480.

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References 29

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方法	RRMSE/%	SSIM
插值采样方法	36.09	0.7250
广义逆矩阵方法	21.67	0.7566
完全QR分解方法	14.71	0.8076

方法	RRMSE/%	SSIM
插值采样方法	21.38	0.7924
广义逆矩阵方法	12.64	0.8240
完全QR分解方法	4.24	0.8402

目标过采样率	仿真图像分辨率/m	仿真图像尺寸/像素	重建图像分辨率/m	重建图像尺寸/像素
3	4	43×66	2.4	72×37
2	4	43×53	2.6	65×40
1.5	4	43×44	3.0	58×39

方法	RRMSE/%	SSIM
插值采样方法	32.40	0.7221
广义逆矩阵方法	20.66	0.7349
完全QR分解方法	7.05	0.8064

算法	RRMSE/%	SSIM
插值采样方法	38.14	0.6780
广义逆矩阵方法	30.79	0.6989
完全QR分解方法	10.43	0.7728

Synergistic super-resolution reconstruction via QR decomposition and POCS

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