Computational Plenoptic Imaging
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Volume xx (200y), Number z, pp. 1–25 Computational Plenoptic Imaging Gordon Wetzstein1, Ivo Ihrke2, Douglas Lanman3, and Wolfgang Heidrich1 1University of British Columbia 2Universität des Saarlandes / MPI Informatik 3MIT Media Lab Abstract The plenoptic function is a ray-based model for light that includes the color spectrum as well as spatial, temporal, and directional variation. Although digital light sensors have greatly evolved in the last years, one fundamental limitation remains: all standard CCD and CMOS sensors integrate over the dimensions of the plenoptic func- tion as they convert photons into electrons; in the process, all visual information is irreversibly lost, except for a two-dimensional, spatially-varying subset — the common photograph. In this state of the art report, we review ap- proaches that optically encode the dimensions of the plenoptic function transcending those captured by traditional photography and reconstruct the recorded information computationally. Categories and Subject Descriptors (according to ACM CCS): I.4.1 [Image Processing and Computer Vision]: Dig- itization and Image Capture— I.4.5 [Image Processing and Computer Vision]: Reconstruction— 1. Introduction spectral, and directional resolution are manifold; medical Evolution has resulted in the natural development of a va- imaging, remote sensing, shape reconstruction, surveillance, riety of highly specialized visual systems among animals. and automated fabrication are only a few examples. In par- The mantis shrimp retina, for instance, contains 16 different ticular, the computer graphics and vision communities ben- types of photoreceptors [MO99]. The extraordinary anatomy efit from computational plenoptic imaging. Not only do the of their eyes not only allows the mantis shrimp to see 12 dif- techniques discussed in this survey allow highly detailed vi- ferent color channels, ranging from ultra-violet to infra-red, sual information to be captured, which is essential for the and distinguish between shades of linear and circular polar- acquisition of geometry, scene reflectance, materials, and re- ization, but it also allows the shrimp to perceive depth using fractive index properties, but they can also be directly used trinocular vision with each eye. Other creatures of the sea, for image-based rendering and lighting, increasing the real- such as cephalopods [MSH09], are also known to use their ism of synthetically generated content. ability to perceive polarization for communication and un- The plenoptic function [AB91] provides a ray-based veiling transparency of their prey. Although the compound model of light encompassing properties that are of inter- eyes found in flying insects have a lower spatial resolution est for image acquisition. Most of these properties, how- compared to mammalian single-lens eyes, their temporal re- ever, are irreversibly lost by standard sensors integrating solving power is far superior to the human visual system. over the plenoptic dimensions during image acquisition. As Flys, for instance, have a flicker fusion rate of more than illustrated in Figure 1, the plenoptic dimensions include the 200 Hz [Ruc61], which is an order of magnitude higher than color spectrum as well as spatial, temporal, and directional that of the human visual system. light variation. We also consider dynamic range a desirable Traditionally, cameras have been designed to capture what property, as common sensors have a limited dynamic range. a single human eye can perceive: a two-dimensional trichro- In addition to the plenoptic dimensions, we further cat- matic image. Inspired by the natural diversity of perceptual egorize plenoptic image acquisition techniques according systems and fueled by advances of digital camera technol- to their hardware configuration (see Fig. 1). While single- ogy, computational processing, and optical fabrication, im- device, multi-shot approaches are usually the preferred age processing has begun to transcend limitations of film- method for capturing plenoptic properties of static scenes, based analog photography. Applications for the computer- either multiple devices or single-image multiplexing are re- ized acquisition of images with a high spatial, temporal, quired to record dynamic phenomena. Using multiple de- submitted to COMPUTER GRAPHICS Forum (9/2011). 2 G. Wetzstein, I. Ihrke, D. Lanman, and W. Heidrich / Computational Plenoptic Imaging Plenoptic Dimension Acquisition Approach Dynamic Range Color Spectrum Space | Focal Surfaces Directions | Light Fields Time Assorted Pixels Assorted Pixels Color Filter Arrays Coded Apertures Plenoptic Cameras w/ Gradient Camera Flutter Shutter Assorted Pixels Focal Sweep Lenses, Masks, or Mirrors Split Aperture Imaging Reinterpretable Imager Dispersive Optics Field Correction Compound Eye Cameras Adaptive DR Imaging Sensor Motion Single Shot Acquisition Exposure Brackets Narrow Band Filters Focal Stack Programmable Aperture High Speed Imaging Generalized Mosaics Generalized Mosaicing Jitter Camera Camera & Gantry Temporal Dithering HDR Video Agile Spectrum Imaging Super-Resolution Sequential Image Capture Multi-Camera Arrays Multi-Camera Arrays Optical Splitting Trees Multi-Camera Arrays Multi-Camera Arrays Optical Splitting Trees Hybrid Cameras Multi-Device Setup Figure 1: Taxonomy and overview of plenoptic image acquisition approaches. vices is often the most expensive solution, whereas multi- as high-speed events (Section 6). We also outline the acqui- plexing commonly reduces the spatial resolution of captured sition of light properties that are not directly included in the content in favor of increased plenoptic resolution. The opti- plenoptic function, but related, such as polarization, phase mal acquisition approach for a given problem is, therefore, imaging, and time-of-flight (Section 7) and point the reader dependent on the properties of the photographed scene and to more comprehensive literature on these topics. Conclu- the available hardware. sions and possible future avenues of research are discussed in Section 8. 1.1. Computational Photography and Plenoptic Imaging Due to the fact that modern, digital acquisition approaches are often closely related to their analog predecessors, we What makes plenoptic imaging different than general com- outline these whenever applicable. For each of the plenop- putational photography? Plenoptic imaging considers a sub- tic dimensions we also discuss practical applications of the set of computational photography approaches; specifically, acquired data. As there is an abundance of work in this field, those that aim at acquiring the dimensions of the plenoptic we focus on imaging techniques that are designed for stan- function with combined optical light modulation and com- dard planar 2D sensors. We will only highlight examples putational reconstruction. Computational photography has of modified sensor hardware for direct capture of plenoptic grown tremendously in the last years with dozens of pub- image information. We do not cover pure image processing lished papers per year in a variety of graphics, vision, and techniques, such as tone-reproduction, dynamic range com- optics venues. The dramatic rise in publications in this inter- pression and tone-mapping [RWD∗10], or the reconstruction disciplinary field, spanning optics, sensor technology, image of geometry [IKL∗10], BSDFs and reflectance fields. processing, and illumination, has made it difficult to encom- pass all research in a single survey. 2. High Dynamic Range Imaging We provide a structured review of the subset of research that has recently been shown to be closely-related in terms High dynamic range (HDR) image acquisition has been a of optical encoding and especially in terms of reconstruction very active area of research for more than a decade. The algorithms [IWH10]. Additionally, our report serves as a re- dynamic range of an imaging system is commonly defined source for interested parties by providing a categorization of as the ratio of largest and smallest possible value in the recent research and is intended to aid in the identification of range, as opposed to the domain, of a recorded signal. Un- unexplored areas in the field. fortunately, standard sensors have a limited dynamic range, which often results in clipping of bright and dark parts of a photographed scene. HDR imaging is important for many 1.2. Overview and Definition of Scope computer vision applications, including image-based ge- In this report, we review the state of the art in joint opti- ometry, material, and lighting reconstruction. Applications cal light modulation and computational reconstruction ap- for high dynamic range imagery in computer graphics in- proaches for acquiring the dimensions of the plenoptic func- clude physically-based rendering and lighting [Deb02], im- tion. Specifically, we discuss the acquisition of high dy- age editing, digital photography and cinema, perceptual dif- namic range imagery (Section2), the color spectrum (Sec- ference metrics based on absolute luminance [MDMS05, tion3), light fields and directional variation (Section 4), spa- MKRH11], virtual reality, and computer games. With the in- tial super-resolution and focal surfaces (Section 5), as well troduction of the HDR display prototype [SHS∗04] and its submitted to COMPUTER GRAPHICS Forum (9/2011). G. Wetzstein, I. Ihrke, D. Lanman, and W. Heidrich / Computational Plenoptic Imaging 3 successor models becoming consumer products today, high- contrast photographic material is required for a mass market. For