Defects such as inclusions and voids are commonly observed in fiber reinforced polymer (FRP) composites. In order to ensure the quality of FRP products, it is desirable to have reliable and non-destructive testing techniques for detecting defects. Among existing techniques, pulsed thermography (PT) has the advantages of a wide scanning range and simple operation. However, thermal images generated using PT are often noisy, and can contain non-uniform backgrounds resulting from uneven heating. As a result, post-processing is necessary to improve the detection capability of PT. In this study, stable principal component pursuit (SPCP) is integrated with a moving-window strategy to decompose thermographic data into three parts: a low-rank matrix to approximately extract background information, a dense noise term containing most of the measurement noise, and a sparse matrix reflecting the defects in the tested specimen. In this manner, improved detection results can be obtained from the reconstructed thermal images based on the sparse matrix. The effectiveness of the proposed method is illustrated through experiments. (C) 2016 Elsevier Ltd. All rights reserved.