We propose a new iterative greedy algorithm to reconstruct sparse signals in Compressed Sensing. The algorithm, called Conjugate Gradient Hard Thresholding Pursuit (CGHTP), is a simple combination of Hard Thresholding Pursuit (HTP) and Conjugate Gradient Iterative Hard Thresholding (CGIHT). The conjugate gradient method with a fast asymptotic convergence rate is integrated into the HTP scheme that only uses simple line search, which accelerates the convergence of the iterative process. Moreover, an adaptive step size selection strategy, which constantly shrinks the step size until a convergence criterion is met, ensures that the algorithm has a stable and fast convergence rate without choosing step size. Finally, experiments on both Gaussian-signal and real-world images demonstrate the advantages of the proposed algorithm in convergence rate and reconstruction performance.

In laser-MIG hybrid welding, the visual information can be used to monitor the stability of the process and estimate the weld formation. Taking the hybrid welding of ER308 stainless steel as an example, a high-speed camera is used to simultaneously obtain visual information of top and bottom of the weldment during the welding process. Combined with the visual characteristics of arc light, molten hump and spatter at the bottom, the influence of various welding state characteristics on weld formation was analyzed. The comparison between the welding process of good weld and root humps illustrated that the time series curves of the area and centroid coordinates of the molten hump at the bottom can reflect the formation process of the root humps. The main cause of root humps is the lack of heat input. The results support a basis for monitoring and controlling the laser–MIG hybrid welding process status.