To address the problem of miss- and false detection during quality inspection of lithium-ion battery cover screen printing (LBCSP), we propose a hybrid image registration method using a point-based feature extraction algorithm and nonlinear-scale space construction. Our proposed method integrates the accelerated-KAZE algorithm with the boosted efficient binary local image descriptor (BEBLID), and is named A-BEBLID. Facing the challenge of the inevitable offset caused by machine vibration during production, we combine a nonlinear diffusion filter with a local image descriptor to extract features from images, and then use the grid-based motion statistics algorithm to remove the incorrect matching pairs. We tested the method on a custom dataset created using images taken from actual lithium-ion battery production lines, named LBCSP. We also evaluated the method on the public HPatches dataset. The average precision achieved by A-BEBLID on the LBCSP dataset is 89% (threshold: 2 pixels), with a localization error of 1.11 pixels, while on the HPatches dataset, the average precision is 73% (threshold: 2 pixels), with a localization error of 1.52 pixels. Comprehensive experimental results also showed that the proposed A-BEBLID can outperform other approaches found in the literature. The method can be further applied to other industry scenarios with similar image registration requirements.