激光技术2024，Vol.48Issue(4)：491-498,8.DOI:10.7510/jgjs.issn.1001-3806.2024.04.006
基于自适应深度先验的高光谱图像超分辨率
Adaptive deep prior for hyperspectral image super-resolution
摘要
Abstract
To address the issue that the difficult parameter selection or poor interpretability caused by the existing hyperspectral super-resolution methods rely on manual or data-driven prior,a deep prior regularization-based hyperspectral image super-resolution method was adopted for theoretical analysis and experimental validation.First,a multi-stage feature extraction network based on a deep convolutional neural network was designed to extract spatial and spectral information from the degraded images.Then,the collected spatial-spectral prior features were fed into a transformer-based feature fusion module,where complementary information from the spatial and spectral domains was adaptively extracted to capture the image's global prior features.Finally,the super-resolution problem of the image was formulated as an optimization problem by inserting deep prior regularization term in the degraded model,the solution of which can be achieved using the alternating direction method of multipliers while minimizing solution complexity.Experimental results show that reconstruction signal-to-noise ratio of this algorithm is 34.16 dB and 29.35 dB when both of the signal-to-noise ratio are 35 dB,which is 2.78 dB and 2.17 dB higher than the suboptimal algorithm,respectively.The reconstructed high-resolution hyperspectral images have high consistency with their inherent structures under the condition of deep prior regularization.This study provides a reference for the combined use of manual and data-driven prior to enhance the spatial resolution of hyperspectral images.关键词
图像处理/超分辨率重建/深度先验正则/高光谱图像/多光谱图像/交替方向乘子法Key words
image processing/super-resolution reconstruction/deep prior regularization/hyperspectral image/multispectral image/alternating direction method of multipliers分类
计算机与自动化引用本文复制引用
马飞,王芳,霍帅..基于自适应深度先验的高光谱图像超分辨率[J].激光技术,2024,48(4):491-498,8.基金项目
辽宁省教育厅科学研究面上项目(LJKZ0357) （LJKZ0357）
辽宁省科技厅自然科学基金面上项目(2023-MS-314) （2023-MS-314）
