## 1 Introduction
proposed the sign coherence factor total focus method (scf-TFM), studied the ultrasonic Lamb wave imaging when the defect spacing was less than the resolution limit, and concluded that the transverse resolution of scf-TFM is twice that of the full-focus method [15]. [PERSON] et al. proposed a compressed sensing-based ultrasonic phased array (cs-TFM) TFM method. The optimal mesh generation method based on accuracy and speed can ensure imaging accuracy and reduce the TFM imaging time [16]. [PERSON] et al. discussed the influence of elastic anisotropy on the ultrasonic imaging of coating defects. Elastic coating will cause image distortion, and they proposed to enhance the imaging quality of FMC-TFM by using alternative isotropic velocities. This method has universal applicability [17]. [PERSON] believes that when the ultrasonic imaging system uses the dynamic emission characteristics of the overlapping wave field of the transmitted beam to reconstruct the image, it can avoid the imaging effect of the plane angle and has good reception performance [18]. [PERSON] et al. developed an ultrasound system based on GPU cluster parallel mechanisms, which can realize high-speed FMC data acquisition and image acquisition at a high frame rate [19]. [PERSON] et al. developed a real-time TFM imaging architecture based on FPGA using the FMC method, which greatly improves the efficiency of data transmission [20]. [PERSON] et al. have developed a non-real-time ultrasonic array system based on the FMC method, which is superior to the commercial ultrasonic array system in terms of lateral resolution and signal-to-noise ratio but increases the time cost [21]. [PERSON] et al. proposed an adaptive ultrasonic imaging method for anisotropic carbon fiber polymer based on FMC-TFM, which can effectively reconstruct various internal defects, such as cracks and voids [22]. [PERSON] et al. proposed an adaptive ultrasonic imaging method based on the fusion of phased array and full-focus technology and verified the different shape defects with a flexible wedge [23]. It can be seen from the above that the TFM imaging quality greatly affects the recognition rate of defects, especially in the field of industrial detection, where the complex morphology of the tested object surface will lead to worse imaging quality. Therefore, image processing is an important research direction of FMC-TFM ultrasonic testing, in which the elimination of image artifacts and noise reduction can greatly improve image quality. [PERSON] proposed a probe standoff optimization method (PSOM) to solve the problems of artifacts and resolution degradation in the detection of curved objects. By comparing the signal artifact ratio (SAR) and array performance index (API), the feasibility of this method was proved, but its performance for the array performance index of the convex model was poor [24]. By comparing the noise reduction effect of the time reversal multi-signal classification algorithm and full-focus algorithm, [PERSON] et al. found that the time reversal multi-signal classification algorithm has a relatively better effect for high noise processing [25]. [PERSON] et al. proposed a deep learning architecture based on the principle of full focus and self-coding, which can effectively remove and suppress the artifacts of ultrasonic array images, but its applicability is narrow [26]. [PERSON] et al. proposed a delay multiplication sum algorithm based on the TFM, which can effectively use the three-dimensional feature information to generate the frequency component, greatly improve the signal-to-noise ratio of the reconstructed image, and have high lateral resolution [27]. [PERSON] et al. carried out laser ultrasonic total focus detection for welds and determined that the guided wave generated by lasers can be used for TFM remote evaluation, but the image reconstruction effect due to edge reflection and a scattering effect is poor [28]. [PERSON] et al.