The work aims to solve problems of poor correlation between functional units, weak completeness of image chromaticity feature extraction, weak distortion control in super-resolution reconstruction, and residual control in sampling process in super-resolution image reconstruction models. By introducing the double activation function into the convolutional neural network model, the compatibility and connectivity between the functional units in the model were improved. A super-resolution distortion control unit was constructed using a dense connected convolutional neural network to perform convolutional compensation operations on four chromatic components, respectively. The residual interpolation function was applied to the upsampling unit and deep backprojection network rules were used to achieve super-resolution chromaticity feature interpolation operations. The designed model set combined multiple convolutional kernels internally to achieve super-resolution chromaticity distortion compensation. A unified processing weight was used to ensure the organic fusion of the internal components of the entire model. In conclusion, the relevant experimental results verify that the image reconstruction model proposed in this paper has good reliability, stability, and efficiency.