In recent years, we have seen the development of integrated plenoptic sensors, where multiple pixels are placed under one microlens. It is mainly used by cameras and smartphones to drive the autofocus of the main lens, and it often takes the form of dual-pixels with 2 rectangular sub-pixels. We study the evolution of dual-pixels, the so-called quad-pixel sensor with 2x2 square sub-pixels under the microlens. As it is a simple light field capturing device, we investigate the computational photography abilities of such sensor. We first present our work on pixel-level simulations, then we present a model of image formation taking into account the diffraction by the microlens. Finally, we present new ways to process a quad-pixel images based on deep learning.
The aperture of future Extremely Large Telescopes will be composed of hundreds of individual segments which require the development of new robust phasing techniques based on the concept of pupil plane detection. The misalignments of the segments produce amplitude variations at the location of the segment edges recorded on the phasing camera. To analyze the signals which contain the information about the segmentation error, the position of the segment borders on a CCD image must be determined with a sub-pixel accuracy. In the framework of the Active Phasing Experiment (APE) carried out at ESO, we have developed two methods to retrieve the segmented pattern. One is based on the Hough transform and the other one on the correlation of the images with a hexagonal pattern. After a description of both methods, we shall present the results achieved so far with simulations. Finally, the performances of the two methods will be compared.
This project forms part of the ELT Design Study and is supported by the European Commission, within Framework Programme 6, contract No 011863.
This paper presents some of the main aspects of the software library that has been developed for the reduction of optical and infrared images, an integral part of the end-to-end survey system being built to support public imaging surveys at ESO. Some of the highlights of the new library are: unbiased estimates of the background, critical for deep IR observations; efficient and accurate astrometric solutions, using multi-resolution rechniques; automatic identification and masking of satellite tracks; weighted-coaddition of images; creation of optical/IR mosaics, and appropriate management of multi-chip instruments. These various elements have been integrated into a system using XML technology for setting input parameters, driving the various processes, producing comprehensive history logs and storing the results, binding them to the supporting database and to the web. The system has been extensively tested using deep images as well as crowded fields, (e.g., globular clusters LMC), processing at a rate of 0.5 Mega-pixels per second using a DS20E ALPHA computer with two processors. The goal of this presentation is to review some of the main features of this package.
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