The invention provides an image low-rank reconstruction method based on an adaptive graph learning principal component analysis method. Firstly, normalization preprocessing is carried out on input image data; then, an adjacent matrix is calculated based on the relation between image sample points; thirdly, an image low-rank reconstruction network model is built and mainly comprises a graph encoder, a full-connection decoder and a graph decoder, the graph encoder encodes data to obtain depth representation, the depth representation of the data passes through the two decoders at the same time to obtain low-rank reconstruction features and a reconstruction image, Adaptive Loss and Schatten p norms are adopted in a loss function, and meanwhile, the loss between the reconstructed image and an original image is optimized, so that local structure information of the data is kept; and finally, a reconstructed image is obtained through adaptive iteration updating of the adjacent matrix and a network model. The method has good non-linear representation capability and high robustness, and can perform low-rank reconstruction of an image even under the condition that data is interfered by noise.
Image low-rank reconstruction method based on adaptive graph learning principal component analysis method