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Conventional microscopes have a high spatial resolution and a low depth-of-field. Light field microscopes have a high depth-of-field but low spatial resolution. A new hybrid approach uses information from both systems to reconstruct a high-resolution light field [Appl. Opt.58, A142 (2019)APOPAI0003-693510.1364/AO.58.00A142]. The resolution of the resulting light field is said to be limited only by diffraction and the size of the pixels. In this paper, we evaluate this method. Using simulation data we compare the output of the hybrid reconstruction algorithm with its simulated ground truth. Our analyses reveal that the observed improvement in the light field quality is not a consequence of data fusion or incorporation of information from a conventional camera, but rather the results of an intermediate interpolation step within the light field itself. This suggests that the required information is already inherent to the light field. By employing the Richardson-Lucy Light Field Deconvolution algorithm, we demonstrate that existing algorithms have already utilized this information.

Anomaly detection is a critical problem in the manufacturing industry. In many applications, images of objects to be analyzed are captured from multiple perspectives which can be exploited to improve the robustness of anomaly detection. In this work, we build upon the deep support vector data description algorithm and address multi-perspective anomaly detection using three different fusion techniques, i.e., early fusion, late fusion, and late fusion with multiple decoders. We employ different augmentation techniques with a denoising process to deal with scarce one-class data, which further improves the performance (ROC AUC =80%). Furthermore, we introduce the dices dataset, which consists of over 2000 grayscale images of falling dices from multiple perspectives, with 5% of the images containing rare anomalies (e.g., drill holes, sawing, or scratches). We evaluate our approach on the new dices dataset using images from two different perspectives and also benchmark on the standard MNIST dataset. Extensive experiments demonstrate that our proposed multi-perspective approach exceeds the state-of-the-art single-perspective anomaly detection on both the MNIST and dices datasets. To the best of our knowledge, this is the first work that focuses on addressing multi-perspective anomaly detection in images by jointly using different perspectives together with one single objective function for anomaly detection.