In the field of image compression perceptual reconstruction, high-quality images are reconstructed with high similarity to the original image, and details are retained to eliminate artifacts through effective image prior information reconstruction. Thus, aiming at the K-space data with insufficient sampling, based on the classic CNN algorithm CBDNet algorithm, this study adopts the method to combine the advantages of fusing deep learning prior information and traditional image restoration. Meanwhile, a hybrid reconstruction algorithm based on prior denoising of deep neural network and compressed sensing algorithm of BM3D block is studied. The algorithm employs an interactive method to train a multi-scale residual network to suppress noise levels and combines deep learning with the multi-scale matching of traditional blocks to extract image feature data at different scales through optimal selection, thus suppressing artifacts and quickly reconstructing high-quality MRI. The experimental results show that deep learning combined with BM3D can reduce artifacts and retain details in MR image reconstruction, enhancing the reconstruction effect. Additionally, the computational complexity of the algorithm is not much more than that of the single algorithm by the GPU accelerated operation. It can be seen that the hybrid MRI based on convolution blind denoising has a better effect.