Search

Although promising results have been achieved in the restoration of complex illumination images with the Retinex algorithm, there are still some drawbacks in the processing of Retinex. Considering the noise characteristics of complex illumination images, in this study, we propose a novel restoration algorithm for noisy complex illumination, which combines guided adaptive multi‐scale Retinex (GAMSR) and improvement BayesShrink threshold filtering (IBTF) based on double‐density dual‐tree complex wavelet transform (DDDTCWT) domain. Extensive restoration experiments are conducted on three typical types images and the same image with different noises. On the basis of a series of evaluation indexes, we compare our method to those of state‐of‐the‐art algorithms. The results show that (i) SSIM of the proposed IBTF is superior to traditional Bayes threshold method by 15% as the standard variance is 100. (ii) PSNR of the proposed GAMSR enhances 15% to traditional MSR. (iii) The clarity of final results for restoration speeds up three times than that of original images, and the information entropy is improved slightly too. Therefore, the proposed method can effectively enhance the details, edges and textures of the image under complex illumination and noises.

Vision-based welding seam tracking is an important and unique branch of welding automation. Active vision seam tracking systems achieve accurate feature extraction by using an auxiliary light source, but this will introduce extra costs and the real-time performance will be affected. In contrast, passive vision systems achieve better real-time performance and their structure is relatively simple. This paper proposes a passive vision welding seam tracking system in Plasma Arc Welding (PAW) based on semantic segmentation. The BiseNetV2 network is adopted in this paper and online hard example mining (OHEM) is used to improve the segmentation effect. This network structure is a lightweight structure allowing effective image feature extraction. According to the segmentation results, the offset between the welding seam and the welding torch can be calculated. The results of the experiments show that the proposed method can achieve 57 FPS and the average error of the offset calculation is within 0.07 mm, meaning it can be used for real-time seam tracking.