CASSI Hyperspectral Reconstruction for Pharmaceutical Quality Imaging
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Solution Overview
Problem
Existing methods for pharmaceutical quality inspection, such as chemical titration and RGB camera-based visual inspection, are inefficient and lack the capability to analyze spectral features, while hyperspectral imaging is underutilized due to challenges in computationally reconstructing three-dimensional data from two-dimensional measurements in coded aperture snapshot spectral imaging (CASSI) systems.
Innovation Solution
A pharmaceutical hyperspectral reconstruction method using a CASSI system involves data preprocessing, augmentation, simulated spatial encoding, and a deep symmetric neural reconstruction network with spatial-spectral attention mechanisms to reconstruct three-dimensional hyperspectral images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional algorithms (greedy iterative, convex optimization, evolutionary) are used for hyperspectral reconstruction, then the reconstruction process is computationally feasible, but the quality of reconstructed images deteriorates due to reliance on empirically derived priors that cannot capture complex spectral characteristics
Solution Approach 1:
The patent replaces traditional mechanical/mathematical optimization algorithms with a deep learning-based neural network system. The neural network learns spectral-spatial correlations directly from data, substituting the need for manual prior knowledge and iterative mathematical optimization with an automated data-driven approach that captures complex spectral characteristics more effectively.
Solution Approach 2:
The patent transforms the reconstruction approach by changing from fixed empirical priors to adaptive learned priors. The neural network dynamically adjusts its internal parameters (weights and biases) during training to optimize reconstruction quality, allowing the system to adapt to different spectral characteristics without requiring manual tuning of algorithmic parameters.
2Productivity
If CASSI system is used to capture hyperspectral data, then spatial and spectral information is captured simultaneously in a single snapshot, but the reconstruction problem becomes highly ill-posed with more unknowns than equations
Solution Approach 1:
The patent implements a feedback mechanism through the neural network's loss function, which continuously compares reconstructed hyperspectral images with ground truth data during training. This feedback loop allows the network to iteratively refine its reconstruction algorithm, learning to reliably invert the CASSI measurement process despite the ill-posed nature of the problem.
Solution Approach 2:
The patent performs preliminary training of the neural network on a dataset of known spectral signatures and corresponding CASSI measurements before actual reconstruction. This preliminary action prepares the network with learned priors about spectral characteristics, enabling it to handle the ill-posed reconstruction problem effectively when processing new data.
3Measurement precision
If deep learning algorithms are used for hyperspectral reconstruction, then spectral-spatial similarities and structural similarities can be learned to enhance reconstruction quality, but the requirement for large datasets and computational resources increases
Solution Approach 1:
The patent segments the reconstruction task into distinct neural network modules: an encoder that extracts spectral-spatial features, a decoder that reconstructs the hyperspectral cube, and attention mechanisms that focus on critical spectral-spatial correlations. This segmentation allows each module to be optimized independently and trained efficiently, reducing overall computational requirements while maintaining high reconstruction accuracy.
Data Source
AI summary
A pharmaceutical hyperspectral reconstruction method based on a coded aperture snapshot spectral imaging (CASSI) system includes: collecting and processing original pharmaceutical hyperspectral images to obtain augmented pharmaceutical hyperspectral images; performing simulated spatial encoding on the augmented pharmaceutical hyperspectral images to obtain encoded measurement images; performing spectral inverse shift on the encoded measurement images, then performing inverse encoding to obtain inversely encoded three-dimensional hyperspectral images, using the augmented pharmaceutical hyperspectral images as target images, and constructing a training set and a testing set according to the inversely encoded three-dimensional hyperspectral images and the target images; constructing a deep symmetric neural reconstruction network, and training and testing the deep symmetric neural reconstruction network; and deploying a tested deep symmetric neural reconstruction network onto the CASSI system, real-time collecting pharmaceutical measurement images using the snapshot coded imaging system, and performing computational reconstruction on the pharmaceutical measurement images to obtain reconstructed three-dimensional hyperspectral images.