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Recent research in metasurface have demonstrated its potential for enhancing terahertz applications by enabling precise control over electromagnetic waves. However, traditional methods for image reconstruction often depend on theoretical calculation data, which are susceptible to environmental noise and model inaccuracies. To solve this problem, this article proposes an unsupervised learning method that iteratively corrects these errors through periodic training. This method models the physical process as a constraint in the training work, starting with theoretical calculations of the radiation field matrix to predict scattering coefficients, which reduces data requirements and enhances robustness. 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