Common defects in 3D printed carbon fiber reinforced composite components include cracks, bubbles, delamination, and layering issues. However, traditional infrared thermography processing techniques face problems such as edge blurring and artifacts, and they do not fully utilize the temporal information of pixel values. Therefore, this paper proposes an adaptive median filtering algorithm based on temporal information. By combining changes in the current pixel over time and using the z-score outlier removal method, the algorithm assesses whether the current pixel is anomalous within a specific time frame, thereby reducing noise interference. Experimental results demonstrate that this algorithm achieves an average signal-to-noise ratio that is 6-155 dB higher than methods such as wavelet denoising and tensor principal component analysis, while maintaining good edge definition. Additionally, defects were quantified using the half-width measurement method, maximum inter-class variance method, and Gaussian Laplacian operator. The experiments indicate that the content of carbon fiber and the excitation time significantly affect the accuracy of defect quantification.