Abstract: The change detection of retinal fundus image serial is an important and challenging task in computer-aided diagnosis systems. Major challenges for retinal fundus image serial include few sampling frames and large interference of illumination, making it difficult to obtain a robust background model. A change detection method based on tensor robust principal component analysis (TRPCA) is proposed. The method takes TRPCA as the model, expands the serial background, and uses a tensor decomposition to obtain the change region: First, an image closest to the normal state in the serial is selected as the background model. Then, the single-frame background model is expanded into multi-frame backgrounds by pre-processing so that the background model contains more abundant illumination changes. The whole serial is modeled as a three-dimensional tensor volume. Finally, the time-space continuity of the background model and the change region is constrained by the total variation, and the background model was separated using Tucker decomposition to obtain the change region. The experimental results show that compared with the matrix robust principal component analysis (Matrix RPCA), masked-RPCA, and TRPCA methods without total variation constraints, the TRPCA method with total variation constraints more accurately separated the change region and is more robust to the interference of blood vessels and illumination.