The invention relates to a compression perception image reconstruction algorithm based on depth learning. The method comprises the following steps: S1, preprocessing image data, including extracting gray value of the data and dividing the image into blocks; S2, measuring the segmented image blocks to obtain a measurement matrix; S3: Constructing a 10-layer deep compression perceptual reconstruction network; S4, training the 10-layer network in the depth learning framework; S5, after passing through that depth neural network, obtain the reconstructed image block, and rearranging the image blockaccording to the original row and column value accord to the index; S6, after that image blocks are rearrange to obtain a reconstructed image, a BM3D denoiser is selected to carry out denoising processing on the image, and finally the reconstructed image is obtained. The compression perception image reconstruction algorithm provided by the invention consumes most of time in the network training stage, and the image reconstruction speed is very fast after the network training is completed. The invention replaces the traditional reconstruction algorithm through the depth learning network, but still has good reconstruction accuracy.