Automated optical inspection (AOI) is increasingly advocated for in situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, few, if any, have investigated the high-speed contact image sensors (CIS) (i.e., originally developed for document scanners and multifunction printers) for AM quality monitoring. In addition, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multiscale lenses. Therefore, the objective of this article is to design and develop an AOI system with contact image sensors for multiresolution quality inspection of layerwise builds in additive manufacturing. First, we retrofit the AOI system with contact image sensors in industrially relevant 95 mm/s scanning speed to a laser-powder-bed-fusion (LPBF) machines. Then, we design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, re-coater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Second, layerwise images are pre-preprocessed for alignment, registration, and identification of regions of interests (ROIs) of these nine parts. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. Third, we perform the paired comparison analysis to investigate how different levels of factors influence the distribution of wavelet features. Finally, these features are shown to be effective in predicting the extent of defects in the computed tomography (CT) data of layerwise AM builds. The proposed framework of multiresolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.