Defects may occur in photovoltaic (PV) modules during production and long-term use, thereby threatening the safe operation of PV power stations. Transient thermography is a promising defect detection technology; however, its detection is limited by transverse thermal diffusion. This phenomenon is particularly noteworthy in the panel glasses of PV modules. A dynamic thermography testing method via transient thermography and Wiener filtering deconvolution optimization is proposed. Based on the time-varying characteristics of the point spread function, the selection rules of the first-order difference image for deconvolution are given. Samples with a broken grid and artificial cracks were tested to validate the performance of the optimization method. Compared with the feature images generated by traditional methods, the proposed method significantly improved the visual quality. Quantitative defect size detection can be realized by combining the deconvolution optimization method with adaptive threshold segmentation. For the same batch of PV products, the detection error could be controlled to within 10%.