The compressed sensing multi-scale image reconstruction depth network model designed in this study focuses on solving the problems of insufficient multi-scale features representation and weak adaptive processing ability of the depth reconstruction model.On the premise of introducing a deep convolutional neural network model to extract image features,the spatial pyramid pooling was utilized to improve multi-scale feature description ability,the noise filters were designed to strengthen noise control,and the non-local neural network structure was improved to enhance the adaptive performance of the model.The experimental results showed that the designed image depth reconstruction module has good control over variance,coefficient of variation,and signal-to-noise ratio of the decoded reconstructed image after compression and encoding.The quality evaluation of image phase consistency and gradient features is high.When the sampling rate is between 50%and 60%,the peak signal to noise ratio value is the highest.The relative norm l2 error between the initial image and the decoded reconstructed image is around 0.16,and the peak signal to noise ratio is greater than 46dB,The cosine value of image cosine similarity evaluation is about 0.91,the cosine angle value is about 0.13°,the feature similarity is about 0.80,and the structural similarity ratio is about 0.86.The model designed in this study has strong adaptability,accurate error control,and achieves high fusion between the constituent modules.
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