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Compressed Spectrum Reconstruction Method Based on Coding Feature Vector Enhancement
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DOI：10.1109/TGRS.2023.3347220.png)
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Abstract
En 中文
Compressive spectral imaging (CSI) is a snapshot spectral imaging technique that rapidly captures the spectral information of a target in a single exposure and effectively reconstructs high spectral data using reconstruction algorithms. However, due to the presence of a large number of identical pixels in the measured image, which map to different prior spectral information, existing algorithms struggle to establish an accurate pixel separation representation model. To improve the separation effect between pixels and enhance the representation capability of the measured image pixels, we propose a compressed spectral reconstruction method with enhanced encoding feature vectors. By designing encoding information calculation rules based on a combination of linear and nonlinear functions, encoding features are calculated according to the spatial coordinate position information and wavelength information of the pixels, effectively enhancing the separation representation characteristics between channels and neighboring pixels through the addition of encoding features. Furthermore, by utilizing the semantic similarity between the predicted results of the prior model and the prior spectral image, the reconstruction problem is transformed into a total variation (TV) minimization problem between the predicted results of the prior model and the reconstruction results, combined with the alternating direction method of multipliers (ADMMs) to achieve accurate pixel reconstruction. The experimental setup utilizes a dual-camera compressed spectral imaging (DCCHI) system, consisting of a dual-dispersion coded aperture compressed spectral imaging (DD-CASSI) system and a grayscale imaging system. Various experiments have shown that the proposed method outperforms in reconstructing quality and displays superior algorithmic performance.
Keywords:
Compressed spectral imaging
encoding feature reconstruction
prior spectral data
vector enhancement
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W