Microlens array grid estimation, light field decoding, and calibration

We quantitatively investigate multiple algorithms for microlens array grid estimation for microlens array-based light field cameras. Explicitly taking into account natural and mechanical vignetting effects, we propose a new method for microlens array grid estimation that outperforms the ones previously discussed in the literature. To quantify the performance of the algorithms, we propose an evaluation pipeline utilizing application-specific ray-traced white images with known microlens positions. Using a large dataset of synthesized white images, we thoroughly compare the performance of the different estimation algorithms. As an example, we apply our results to the decoding and calibration of light fields taken with a Lytro Illum camera. We observe that decoding as well as calibration benefit from a more accurate, vignetting-aware grid estimation, especially in peripheral subapertures of the light field.Comment: \copyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Image-based roughness estimation of laser cut edges with a convolutional neural network

Laser cutting of metals is a complex process with many influencing factors. As some of them are subject to change, the cut quality needs to be checked regularly. This paper aims to estimate the roughness of cut edges based on RGB images instead of surface topography measurements. We trained a convolutional neural network (CNN) on a broad database of images and corresponding roughness values. The CNN estimates the roughness well with a mean error of 3.6 µm. Sometimes it is more reliable than the surface measuring device because the RGB images are less prone to reflectivity problems than the measurements

Microlens Array Grid Estimation, Light Field Decoding, and Calibration

We quantitatively investigate multiple algorithms for microlens array grid estimation for microlens array-based light field cameras. Explicitly taking into account natural and mechanical vignetting effects, we propose a new method for microlens array grid estimation that outperforms the ones previously discussed in the literature. To quantify the performance of the algorithms, we propose an evaluation pipeline utilizing application-specific raytraced white images with known microlens positions. Using a large dataset of synthesized white images, we thoroughly compare the performance of the different estimation algorithms. As an example, we apply our results to the decoding and calibration of light fields taken with a Lytro Illum camera. We observe that decoding as well as calibration benefit from a more accurate, vignetting-aware grid estimation, especially in peripheral subapertures of the light field

Algorithms for microlens center detection

Abstract We investigate four algorithms for microlens center detection, two of which have not been previously discussed in the literature. Using a physical approach, we create a set of 81 synthetic white images with known microlens center coordinates. Applying the different detection algorithms to the synthetic white images, we are able to quantitatively evaluate their respective performance in terms of accuracy, precision and recall. Overall, the proposed methods outperform the ones that have been previously published

Methods for the localization of supporting slats of laser cutting machines in single images

The supporting slats of laser flatbed machines cause process reliability problems, such as tilted parts colliding with the cutting head. In order to mitigate these problems the position of the supporting points for a part to be cut must be known, before the machines numerical control program can be changed accordingly. Being able to detect the position of supporting slats accurately is necessary to do that. This work compares image processing methods to localize the supporting slats in single images. The best features are based on filters in the frequency domain and can have accuracies above 96 %