In view of the problem of infrared nondestructive testing for unbounded defects of glass fiber reinforced plastic laminates, a kind of artificial unbounded defect sample is prepared firstly. The unbounded defects are detected by infrared pulse thermal wave imaging technology, and the transient response process of surface thermal signals in unbounded area and non debonding area of laminates is analyzed. The image signalnoise ratio and normalized contrast are used as evaluation indexes to quantitatively analyze the effects of four thermal image reconstruction algorithms, including thermography signal reconstruction, complex modulation ZoomFFT, improved independent component analysis (ICA) and principal component analysis (PCA), and the function of the unbounded defect recognition. On this basis, the PCA algorithm based on thermal signal reconstruction enhancement is proposed, and the effect of the algorithm in unbounded defect recognition is verified. The results show that the four thermal image reconstruction algorithms can improve the quantitative identification ability of unbounded defects, in which the thermal signal reconstruction is the most significant to improve the contrast between the defect area and the non defect area, and the principal component analysis has the strongest ability to suppress the thermal image noise; and the principal component analysis based on the enhancement of thermal signal reconstruction can significantly improve the quantitative identification ability of unbounded defects with the depth of 0.5 mm, 1.0 mm and 1.5 mm.