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Painting Psychotherapy Image Measurement and Reconstruction Algorithm based on Improved Hadamard Matrix

机译:基于改进的Hadamard矩阵的绘画心理治疗图像测量与重建算法

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Painting psychotherapy based on Internet technology has become a method of mental health testing and treatment. This method will produce a large number of digital images of painting psychotherapy. How to measure and reconstruct the image is the basis of automatic analysis and processing. This study aimed to propose a painting psychotherapy image measurement and reconstruction algorithm based on the improved Hadamard matrix. Moreover, the construction of a measurement matrix was analyzed using the compressed sensing theory and the Hadamard matrix construction algorithm was improved and optimized. Also, four different measurement matrices were compared and analyzed. The findings revealed that the improved Hadamard measurement matrix achieved good experimental results in terms of signal reconstruction accuracy and Peak Signal-to-Noise Ratio (PSNR) value. In the experimental analysis of painting psychotherapy image reconstruction, the reconstruction algorithm based on the improved Hadamard measurement matrix had a shorter reconstruction time and the algorithm achieved satisfactory results in terms of the number of iterations and the PSNR value of the reconstructed image. For painting psychotherapy images, the algorithm provided a theoretical basis for later digital processing, large-sample training and analysis and automatic machine recognition and judgment.
机译:基于互联网技术的绘画心理治疗已成为一种心理健康检测和治疗方法。这种方法将产生大量的绘画心理疗法的数字图像。如何测量和重建图像是自动分析和处理的基础。本研究旨在提出基于改进的Hadamard矩阵的绘画心理治疗图像测量和重建算法。此外,使用压缩感测理论分析了测量矩阵的结构,提高了Hadamard矩阵施工算法并优化。此外,比较和分析了四种不同的测量基质。结果表明,改进的Hadamard测量矩阵在信号重建精度和峰值信噪比(PSNR)值方面实现了良好的实验结果。在绘画心理治疗图像重建的实验分析中,基于改进的Hadamard测量矩阵的重建算法具有较短的重建时间,并且在重建图像的迭代次数和PSNR值方面实现了令人满意的结果。对于绘画心理疗法图像,该算法为后来的数字加工,大型培训和分析和自动机器识别和判断提供了理论依据。

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