Real-time refocusing using an FPGA-based standard plenoptic camera

Plenoptic cameras are receiving increased attention in scientific and commercial applications because they capture the entire structure of light in a scene, enabling optical transforms (such as focusing) to be applied computationally after the fact, rather than once and for all at the time a picture is taken. In many settings, real-time inter active performance is also desired, which in turn requires significant computational power due to the large amount of data required to represent a plenoptic image. Although GPUs have been shown to provide acceptable performance for real-time plenoptic rendering, their cost and power requirements make them prohibitive for embedded uses (such as in-camera). On the other hand, the computation to accomplish plenoptic rendering is well structured, suggesting the use of specialized hardware. Accordingly, this paper presents an array of switch-driven finite impulse response filters, implemented with FPGA to accomplish high-throughput spatial-domain rendering. The proposed architecture provides a power-efficient rendering hardware design suitable for full-video applications as required in broadcasting or cinematography. A benchmark assessment of the proposed hardware implementation shows that real-time performance can readily be achieved, with a one order of magnitude performance improvement over a GPU implementation and three orders ofmagnitude performance improvement over a general-purpose CPU implementation

Light field super resolution through controlled micro-shifts of light field sensor

Light field cameras enable new capabilities, such as post-capture refocusing and aperture control, through capturing directional and spatial distribution of light rays in space. Micro-lens array based light field camera design is often preferred due to its light transmission efficiency, cost-effectiveness and compactness. One drawback of the micro-lens array based light field cameras is low spatial resolution due to the fact that a single sensor is shared to capture both spatial and angular information. To address the low spatial resolution issue, we present a light field imaging approach, where multiple light fields are captured and fused to improve the spatial resolution. For each capture, the light field sensor is shifted by a pre-determined fraction of a micro-lens size using an XY translation stage for optimal performance

Light Field compression and manipulation via residual convolutional neural network

Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF\u27s, using simulation, it is possible to explore a high-quality captured LF\u27s properties. The immediate concerns following the LF capture are its storage and processing time. A rich LF occupies a large chunk of memory ---order of multiple gigabytes per LF---. Also, most feature extraction techniques associated with LF postprocessing involve multi-dimensional integration that requires access to the whole LF and is usually time-consuming. Recent advancements in computer processing units made it possible to simulate realistic images using physical-based rendering software. In this work, at first, a transformation function is proposed for building a camera array (CA) to capture the same portion of LF from a scene that a standard plenoptic camera (SPC) can acquire. Using this transformation, LF simulation with similar properties as a plenoptic camera will become trivial in any rendering software. Artificial intelligence (AI) and machine learning (ML) algorithms ---when deployed on the new generation of GPUs--- are faster than ever. It is possible to generate and train large networks with millions of trainable parameters to learn very complex features. Here, residual convolutional neural network (RCNN) structures are employed to build complex networks for compression and feature extraction from an LF. By combining state-of-the-art image compression and RCNN, I have created a compression pipeline. The proposed pipeline\u27s bit per pixel (bpp) ratio is 0.0047 on average. I show that with a 1% compression time cost and 18x speedup for decompression, our methods reconstructed LFs have better structural similarity index metric (SSIM) and comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art video compression techniques used to compress LFs. In the end, using RCNN, I created a network called RefNet, for extracting a group of 16 refocused images from a raw LF. The training parameters of the 16 LFs are set to (\alpha=0.125, 0.250, 0.375, ..., 2.0) for training. I show that RefNet is 134x faster than the state-of-the-art refocusing technique. The RefNet is also superior in color prediction compared to the state-of-the-art ---Fourier slice and shift-and-sum--- methods

A Pipeline for Lenslet Light Field Quality Enhancement

In recent years, light fields have become a major research topic and their applications span across the entire spectrum of classical image processing. Among the different methods used to capture a light field are the lenslet cameras, such as those developed by Lytro. While these cameras give a lot of freedom to the user, they also create light field views that suffer from a number of artefacts. As a result, it is common to ignore a significant subset of these views when doing high-level light field processing. We propose a pipeline to process light field views, first with an enhanced processing of RAW images to extract subaperture images, then a colour correction process using a recent colour transfer algorithm, and finally a denoising process using a state of the art light field denoising approach. We show that our method improves the light field quality on many levels, by reducing ghosting artefacts and noise, as well as retrieving more accurate and homogeneous colours across the sub-aperture images.Comment: IEEE International Conference on Image Processing 2018, 5 pages, 7 figure

Efficient and Accurate Disparity Estimation from MLA-Based Plenoptic Cameras

This manuscript focuses on the processing images from microlens-array based plenoptic cameras. These cameras enable the capturing of the light field in a single shot, recording a greater amount of information with respect to conventional cameras, allowing to develop a whole new set of applications. However, the enhanced information introduces additional challenges and results in higher computational effort. For one, the image is composed of thousand of micro-lens images, making it an unusual case for standard image processing algorithms. Secondly, the disparity information has to be estimated from those micro-images to create a conventional image and a three-dimensional representation. Therefore, the work in thesis is devoted to analyse and propose methodologies to deal with plenoptic images. A full framework for plenoptic cameras has been built, including the contributions described in this thesis. A blur-aware calibration method to model a plenoptic camera, an optimization method to accurately select the best microlenses combination, an overview of the different types of plenoptic cameras and their representation. Datasets consisting of both real and synthetic images have been used to create a benchmark for different disparity estimation algorithm and to inspect the behaviour of disparity under different compression rates. A robust depth estimation approach has been developed for light field microscopy and image of biological samples

Non-disruptive use of light fields in image and video processing

In the age of computational imaging, cameras capture not only an image but also data. This captured additional data can be best used for photo-realistic renderings facilitating numerous post-processing possibilities such as perspective shift, depth scaling, digital refocus, 3D reconstruction, and much more. In computational photography, the light field imaging technology captures the complete volumetric information of a scene. This technology has the highest potential to accelerate immersive experiences towards close-toreality. It has gained significance in both commercial and research domains. However, due to lack of coding and storage formats and also the incompatibility of the tools to process and enable the data, light fields are not exploited to its full potential. This dissertation approaches the integration of light field data to image and video processing. Towards this goal, the representation of light fields using advanced file formats designed for 2D image assemblies to facilitate asset re-usability and interoperability between applications and devices is addressed. The novel 5D light field acquisition and the on-going research on coding frameworks are presented. Multiple techniques for optimised sequencing of light field data are also proposed. As light fields contain complete 3D information of a scene, large amounts of data is captured and is highly redundant in nature. Hence, by pre-processing the data using the proposed approaches, excellent coding performance can be achieved.Im Zeitalter der computergestützten Bildgebung erfassen Kameras nicht mehr nur ein Bild, sondern vielmehr auch Daten. Diese erfassten Zusatzdaten lassen sich optimal für fotorealistische Renderings nutzen und erlauben zahlreiche Nachbearbeitungsmöglichkeiten, wie Perspektivwechsel, Tiefenskalierung, digitale Nachfokussierung, 3D-Rekonstruktion und vieles mehr. In der computergestützten Fotografie erfasst die Lichtfeld-Abbildungstechnologie die vollständige volumetrische Information einer Szene. Diese Technologie bietet dabei das größte Potenzial, immersive Erlebnisse zu mehr Realitätsnähe zu beschleunigen. Deshalb gewinnt sie sowohl im kommerziellen Sektor als auch im Forschungsbereich zunehmend an Bedeutung. Aufgrund fehlender Kompressions- und Speicherformate sowie der Inkompatibilität derWerkzeuge zur Verarbeitung und Freigabe der Daten, wird das Potenzial der Lichtfelder nicht voll ausgeschöpft. Diese Dissertation ermöglicht die Integration von Lichtfelddaten in die Bild- und Videoverarbeitung. Hierzu wird die Darstellung von Lichtfeldern mit Hilfe von fortschrittlichen für 2D-Bilder entwickelten Dateiformaten erarbeitet, um die Wiederverwendbarkeit von Assets- Dateien und die Kompatibilität zwischen Anwendungen und Geräten zu erleichtern. Die neuartige 5D-Lichtfeldaufnahme und die aktuelle Forschung an Kompressions-Rahmenbedingungen werden vorgestellt. Es werden zudem verschiedene Techniken für eine optimierte Sequenzierung von Lichtfelddaten vorgeschlagen. Da Lichtfelder die vollständige 3D-Information einer Szene beinhalten, wird eine große Menge an Daten, die in hohem Maße redundant sind, erfasst. Die hier vorgeschlagenen Ansätze zur Datenvorverarbeitung erreichen dabei eine ausgezeichnete Komprimierleistung

Light field image processing : overview and research issues

Light field (LF) imaging first appeared in the computer graphics community with the goal of photorealistic 3D rendering [1]. Motivated by a variety of potential applications in various domains (e.g., computational photography, augmented reality, light field microscopy, medical imaging, 3D robotic, particle image velocimetry), imaging from real light fields has recently gained in popularity, both at the research and industrial level.peer-reviewe