Objective Carbon Fiber Reinforced Polymer (CFRP) is susceptible to damage defects such as fiber breakage, matrix cracking, and delamination under low-velocity impact loading. Efficient and reliable non-destructive testing methods are crucial to ensuring the structural safety of CFRP components. Infrared thermal wave imaging detection technology has the advantages of non-contact operation, rapid imaging, and large-area inspection. However, the detection images are easily interfered by uneven heating, uneven emissivity, and background noise, resulting in problems such as low signal-to-noise ratio and blurred defect edge features in the detection images. To address the above issues, this study proposed an adaptive image enhancement method integrating Chirp correlation feature extraction and Block-Matching and 3D filtering (BM3D), which provides new insights for the research on adaptive enhancement technology of infrared thermal wave imaging for CFRP impact damage.

Methods A Chirp-modulated infrared thermal wave detection system was constructed (Fig.1), and a drop-weight impact testing machine was adopted to prepare CFRP impact damage specimens with an impact energy of 23 J (Fig.2). The Chirp correlation algorithm was utilized to extract thermal wave phase features to suppress the interference from uneven emissivity. Based on the local noise statistical characteristics of the feature image, an adaptive noise reduction threshold function for the BM3D algorithm was constructed to realize dynamic adaptive enhancement for images with different noise intensities. Gaussian noise of different intensities was added to the normalized Chirp correlation phase feature images, and the defect signal-to-noise ratio (DSNR), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) of the adaptive BM3D algorithm were compared and analyzed to quantitatively evaluate the comprehensive performance of the proposed adaptive enhancement algorithm.

Results and Discussions The research results showed that compared with Fourier spectrum feature images, the Chirp correlation phase feature images exhibited superior imaging quality, and the uneven emissivity in the optimal defect signal-to-noise ratio image was effectively suppressed (Fig.3). Figure6 presented the noise reduction effects of median filtering, Wiener filtering, and the adaptive BM3D algorithm on Chirp correlation phase feature images under different noise intensities. As the noise intensity increased, the noise reduction capabilities of traditional median filtering and Wiener filtering decreased significantly, leading to blurred defect edge features. Figure7-Figure9 illustrated the comparison results of DSNR, PSNR, and SSIM of the three algorithms under different noise intensities. It could be observed from the experimental results that as the normalized noise intensity increased, the DSNR, PSNR, and SSIM of the median filtering and Wiener filtering algorithms showed a significant downward trend, while those of the adaptive BM3D algorithm did not present a significant decline, with the overall performance significantly outperforming the traditional median filtering and Wiener filtering algorithms. This indicates that the adaptive BM3D algorithm can achieve effective noise reduction for the Chirp correlation phase map of CFRP impact damage defects, and possesses excellent edge feature retention capability.

Conclusions Aiming at the adaptive enhancement requirements of infrared thermal wave imaging detection images for CFRP impact damage defects, this paper proposed an adaptive image enhancement method integrating Chirp correlation feature extraction and BM3D filtering. The research showed that under the strong noise condition with normalized noise standard deviation σ=0.2, the defect signal-to-noise ratio (DSNR) of the adaptive BM3D enhancement method proposed in this paper reached 44.41, the peak signal-to-noise ratio (PSNR) was 32.25 dB, and the structural similarity (SSIM) was 0.87. All indicators were significantly better than those of the traditional median filtering and Wiener filtering algorithms, verifying the effectiveness and stability of the proposed method in noise reduction and defect edge feature preservation in strong noise environments. This research provides new ideas for the research on adaptive enhancement technology of infrared thermal wave imaging for CFRP impact damage, and can provide reliable technical support for the engineering application of this technology.