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The diffractive plenoptic camera (DPC) was developed as a system that would capture the spectral and spatial information of a scene in one snapshot. While the DPC couples a diffractive optic with plenoptic camera designs to provide snapshot spectral imaging capabilities, it produces rendered images with low pixel count and low spatial resolution. A modified setup of the DPC, the intermediate image (II)DPC, was built and tested for the first time and compared to both the DPC and a diffractive-optic camera as a system that could improve the cutoff spatial frequency of the rendered images. This paper reports on the spatial resolution achieved for different configurations of the IIDPC and looks at the factors limiting performance. The IIDPC improved on the cutoff spatial resolution over the DPC over a wavelength range of 750 to 790 nm for a design wavelength of 770 nm and improved resolution over a diffractive-optic camera at wavelengths below 750 nm or above 790 nm, with the best results achieved for IIDPC configurations with the largest magnification. Frequency analysis of each system determined that the optic limiting performance was the microlens array. Models showed that decreasing the microlens size improved resolution but reduced the spectral range for the DPC, while decreasing the f/# of the microlenses improved the resolution for the IIDPC. These results will help optimize the designs of future systems. [ABSTRACT FROM AUTHOR]

In the past few years, a new type of camera has been emerging on the market: a digital camera capable of capturing both the intensity of the light emanating from a scene and the direction of the light rays. This camera technology called a light-field camera uses an array of lenses placed in front of a single image sensor, or simply, an array of cameras attached together. An optical device is proposed: a four minilens ring that is inserted between the lens and the image sensor of a digital camera. This device prototype is able to convert a regular digital camera into a light-field camera as it makes it possible to record four subaperture images of the scene. It is a compact and cost-effective solution to perform both postcapture refocusing and depth estimation. The minilens ring makes also the plenoptic camera versatile; it is possible to adjust the parameters of the ring so as to reduce or increase the size of the projected image. Together with the proof of concept of this device, we propose a method to estimate the positions of each optical component depending on the observed scene (object size and distance) and the optics parameters. Real-world results are presented to validate our device prototype. [ABSTRACT FROM AUTHOR]

Light field cameras are an emerging imaging device for acquiring 3-D information of a scene by capturing a field of light rays traveling in space. As light field cameras become portable, hand-held, and affordable, their potential as a 3-D measurement instrument is growing in many applications, including 3-D evidence imaging in crime scene investigations. We evaluated the lateral resolution of commercially available light field cameras, which is one of the fundamental specifications of imaging instruments. For the evaluation of the camera's lateral resolution, we imaged Siemens stars under various imaging configurations and experimental conditions, including changes in distance between the camera and the resolution target plate, illumination, zoom level, location of the Siemens star in the camera's field-of-view, and cameras of the same model. The analysis results from a full factorial experiment showed that (i) when a lower zoom level of the camera was used, the lateral resolution tended not to be affected by distance; however, when a higher zoom level was used, it tended to decrease significantly with respect to the distance, (ii) the center region of the camera's field-of-view provided a better lateral resolution than the peripheral regions, (iii) a higher zoom level yielded a higher lateral resolution, (iv) the two cameras of the same model used in the study did not show a significant difference in the lateral resolution, and (v) changes in illumination did not affect the lateral resolution of the cameras. [ABSTRACT FROM AUTHOR]

Multicamera arrays are increasingly employed in both consumer and industrial applications, and various passive techniques are documented to estimate depth from such camera arrays. Current depth estimation methods provide useful estimations of depth in an imaged scene but are often impractical due to significant computational requirements. This paper presents a framework that generates a high-quality continuous depth map from multicamera array/light-field cameras. The proposed framework utilizes analysis of the local epipolar plane image to initiate the depth estimation process. The estimated depth map is then refined using total variation minimization based on the Fenchel–Rockafellar duality. Evaluation of this method based on a well-known benchmark indicates that the proposed framework performs well in terms of accuracy when compared with the top-ranked depth estimation methods and a baseline algorithm. The test dataset includes both photorealistic and nonphotorealistic scenes. Notably, the computational requirements required to achieve an equivalent accuracy are significantly reduced when compared with the top algorithms. As a consequence, the proposed framework is suitable for deployment in consumer and industrial applications. [ABSTRACT FROM AUTHOR]

Light-field optics is used in light-field cameras to alter the focus condition of an image after it is obtained. This unique function implies that information captured with a light-field camera is not a “picture”; rather, it is a real object image. Thus, a two-dimensional (2-D) image of any focus condition can be generated from the acquired data. An explanation is provided on how a light-field camera records or encodes a three-dimensional (3-D) scene onto a 2-D plane as light-field data, and how the data are decoded back into a 3-D image with a light-field display. Other unique applications based on light-field optics are discussed. Unlike conventional optical systems, light-field optics does not require optical conjugation between the display plane and image plane. Thus, light-field optics can provide an effective means of advancement for conventional optical instruments. To demonstrate the potential applications of light-field optics, we developed a light-field camera and display. [ABSTRACT FROM AUTHOR]

An approach for carrying out depth profile measurement of an object with the plenoptic camera is proposed. A single plenoptic image consists of multiple lenslet images. To begin with, these images are processed directly with a refocusing technique to obtain the depth map, which does not need to align and decode the plenoptic image. Then, a linear depth calibration is applied based on the optical structure of the plenoptic camera for depth profile reconstruction. One significant improvement of the proposed method concerns the resolution of the depth map. Unlike the traditional method, our resolution is not limited by the number of microlenses inside the camera, and the depth map can be globally optimized. We validated the method with experiments on depth map reconstruction, depth calibration, and depth profile measurement, with the results indicating that the proposed approach is both efficient and accurate. [ABSTRACT FROM AUTHOR]

A method is proposed for depth extraction of low-texture region with a light field (LF) camera, which expands the application. Based on the analysis of LF data, it is proven that the depth information can be estimated from a single LF image. Furthermore, as the lenslet LF data can be decoded into a subimages array, and the relationship among subimages is proven to be affine transformation, we used the geometry relationship, which is represented by partition ratio of triangle grids area, to replace the unreliable gray value of low-texture region for stereo matching. In addition, to obtain accurate ratio values, preset points are projected to enrich the texture with a projector, which is convenient and reliable. The proposed method improves the accuracy of the depth extraction obviously at low-texture region compared with the traditional state-of-the-art LF method, and results are validated by experiments. [ABSTRACT FROM AUTHOR]

The traditional methods of depth reconstruction from light fields have excellent performance in depth estimation; however, the depth information has nonlinear scale ambiguity. We divide the three-dimensional metric information into two easy subproblems instead of a whole question. A two-step method is proposed to obtain the metric depth and the metric lateral information, respectively. The depth model is proposed using the refocus property of the light field and the Gaussian lens formula. The model of the lateral coordinate is reasoned from the relationship between the light field in the scene and the recorded light field. Then, three calibration methods are proposed to determine these five parameters (the center position of themicrolens for recovering the four-dimensional light field, the camera principal point, and the initial image distance). The experiments are conducted in our developed light field camera. The precision performance of our method is also confirmed in measuring the calibration board. When the proposed method is adopted to reconstruct the natural scene, the preferable results are obtained. [ABSTRACT FROM AUTHOR]

An automatic calibration method is proposed for a microlens-based plenoptic camera. First, all microlens images on the white image are searched and recognized automatically based on digital morphology. Then, the center points of microlens images are rearranged according to their relative position relationships. Consequently, the microlens images are located, i.e., the plenoptic camera is calibrated without the prior knowledge of camera parameters. Furthermore, this method is appropriate for all types of microlens-based plenoptic cameras, even the multifocus plenoptic camera, the plenoptic camera with arbitrarily arranged microlenses, or the plenoptic camera with different sizes of microlenses. Finally, we verify our method by the raw data of Lytro. The experiments show that our method has higher intelligence than the methods published before. [ABSTRACT FROM AUTHOR]

Multifocus light field cameras are increasingly gaining attention in the field of computational imaging and machine vision due to their ability to capture spatial and angular information of light rays. Accurate calibration of the geometric parameters of multifocus light field cameras is the prerequisite for implementing computational imaging techniques, such as digital refocusing, depth reconstruction, and three-dimensional reconstruction. We propose an improved blur circle detection method for the calibration of multifocus light field cameras. The circular Hough transform-based circle detection method is used to obtain the subimage centers accurately in white raw images. On this basis, the subimages in the checkerboard images are classified, and the image corners are detected to determine the clusters of image corners corresponding to checkerboard corners. Then, the centers of plenoptic disc features are used as projection points of checkerboard corners on the light field image to calculate the camera pose. Levenberg–Marquardt method is further used to solve the calibration model. Finally, the calibration experiments of multifocus light field cameras are carried out to evaluate the improved blur circle detection method. Experimental results indicated that the improved blur circle detection method has higher calibration accuracy of multifocus light field camera than the blur circle detection method, while the mean reprojection error of the microlens centers and the image corners are 0.12 and 0.33 pixels, and the mean relative distance error between adjacent checkerboards is 2.53%. [ABSTRACT FROM AUTHOR]

Light-field cameras are gaining attention for their unique light gathering and post-capture processing capabilities. In our previous work, we combined light-field imaging with structured light technology to realize three-dimensional (3D) reconstruction for highly reflective surface. However, due to the lack of effective calibration of the light-field camera and reconstruction systems, it cannot meet the requirements of precise 3D measurement. A universal sub-aperture image extraction algorithm for the light-field camera with hexagonal microlens is presented to accurately extract the multidirectional images. Then, we explore the spatial relationship in equivalent camera array (ECA) of focused light-field camera according to the perspective variation of sub-aperture images. After that, an accurate 3D measurement system based on ECA model is proposed to achieve 3D measurement for highly reflective surface. To test the system's practical performance, a precision analysis is conducted for a standard gauge block with ground truth. By comparing the reconstruction result of traditional method and ours, we demonstrated the validity of the proposed method to perform 3D measurement for highly reflective surface. [ABSTRACT FROM AUTHOR]

To observe targets in expanded depths of view in light field imaging, we design an open-source light field parallel refocusing code-free GUI tool named OpenRefocus, which is based on classical spatial projection integration. OpenRefocus has been successfully used for refocusing light field images selected from open-source datasets as well as captured using commercial light field cameras faster than other released light field refocusing programs. Considering its fast processing speed and user-friendly operations, OpenRefocus can be a useful tool for light field refocusing in many light field imaging applications. [ABSTRACT FROM AUTHOR]

Light-field and plenoptic cameras are widely available today. Compared with monocular cameras, these cameras capture not only the intensity but also the direction of the light rays. Due to this specificity, light-field cameras allow for image refocusing and depth estimation using a single image. However, most of the existing depth estimation methods using light-field cameras require a prior complex calibration phase and raw data preprocessing before the desired algorithm is applied. We propose a homography-based method with plenoptic camera parameters calibration and optimization, dedicated to our homography-based micro-images matching algorithm. The proposed method works on debayerred raw images with vignetting correction. The proposed approach directly links the disparity estimation in the 2D image plane to the depth estimation in the 3D object plane, allowing for direct extraction of the real depth without any intermediate virtual depth estimation phase. Also, calibration parameters used in the depth estimation algorithm are directly estimated, and hence no prior complex calibration is needed. Results are illustrated by performing depth estimation with a focused light-field camera over a large distance range up to 4 m. [ABSTRACT FROM AUTHOR]

The application of a light-field camera based on an overlapped square aperture micro-lens array (MLA) is reported. The location and direction of the incident light rays are recorded and reconstructed with a large depth of field by this imaging system. The optimized square MLA for light-field imaging, which increases the fill factor and utilization efficiency of the image sensor, is proposed. The array is fabricated by ultra-precision machining and micro-injection molding, which is economical and ensures that the array remains in high accuracy and consistency. Finally, a light-field imaging system is built with lens groups, an MLA, and an adjustable square stop. A comparative study of the field in-depth and super-resolution image is conducted. The experimental results show that the system significantly increases the range of the depth of the field. The reconstructed image is also enhanced with higher resolution and clarity. [ABSTRACT FROM AUTHOR]

For industrial three-dimensional (3D)-measuring applications as well as for other applications such as driver assistance systems in vehicles, a high-quality reproduction in a depth map calculation is of paramount importance. Yet to date, light field cameras still exhibit severe problems in imaging homogeneous surfaces such as smooth metallic surfaces, plastics, or semiconductor materials. On those, the necessary contrast for determining the topography is not present or too weak. Here, we present an innovative approach to improve the performance of light field cameras (also called plenoptic cameras) for various applications in 3D measurement technology. More specifically, we show that this disadvantage can be avoided using structured lighting pattern and analyze various lighting patterns (with respect to geometry, size, regularity, etc.) used to improve the performance of the algorithm in greater detail. In addition, different ways of projecting the pattern have been examined with regard to their advantages and disadvantages. We perform a systematic investigation of the impact of structured light illumination with regard to different object materials, object geometries, and illumination patterns as well as illumination sources. These examinations further comprise the analysis of environmental influences and magnitude of measurement deviations in a light field camera measuring set-up. Summing up, we investigated experimentally the influence of structured illumination on the performance of 3D depth information without the need of additional phase information. [ABSTRACT FROM AUTHOR]

A metric calibration method for light-field three-dimensional (3-D) reconstruction based on plenoptic disk features is presented. Each of these features that relates to a single 3-D point in free physical space could be directly extracted from the raw light field. By establishing the point-to-feature correspondence, the optical parameters of unfocused plenoptic camera would be estimated through nonlinear optimization, which could then be exploited to metrically generate the 3-D point cloud of an object. For verification, all detected plenoptic disk features are reconstructed to 3-D space and then light-field reprojection is performed. In addition, to demonstrate the performance of the proposed method, comparative experiments are carried out on a commercial unfocused plenoptic camera system. Then, the acquired datasets are processed using the published method based on plenoptic disk features and our proposed calibration method, respectively. Experimental results show that the proposed calibration algorithm is more reliable and faster than the existing published one, especially for small field-of-view applications. [ABSTRACT FROM AUTHOR]

Postprocessing of light fields enables us to extract more information from a scene compared to traditional cameras. Plenoptic cameras and camera arrays are two common methods for light field capture. It has been long recognized that the two devices are in some ways equivalent. Practically, both techniques have important constraints. Camera arrays are unable to provide high angular sampling, and the plenoptic camera can have a limited spatial sampling. In simulation, we can easily explore both constraints by simulating two-dimensional viewpoint images and combining them into a four-dimensional light field. We present a transformation for converting between equivalent plenoptic configurations and camera arrays when they capture pristine light fields produced in simulation. We use this approach to simulate light fields of simple scenes and validate our transformation by comparing the focus distance of a standard plenoptic camera and the equivalent camera array's light field. We also show how some simple practical effects can be added to the pristine, synthetic light field via postprocessing and their effect on refocusing distance. [ABSTRACT FROM AUTHOR]

A light-field camera combines optics and computation to provide the ability to perform ranging. Many methods and algorithms, which are time-consuming and noise-sensitive, have been proposed for per-pixel depth estimation. We present a Fourier domain ranging method for light fields (LFs). Instead of estimating the depth for every pixel, we attempt to detect the depths of the different planes at which objects are located. The method is somewhat like using the energy in the focal stack, but it is carried out in a more efficient way. Our method has the advantages of speediness and robustness compared with traditional per-pixel depth estimation methods. In addition to the ranging algorithm, we also demonstrate the idea and application of a region adaptive denoising filter, in which the depth parameters are tuned by the proposed method. We include results for synthetic LFs, the Stanford LF archives, and LFs captured with a Lytro camera, exploiting the algorithm complexity, accuracy, depth resolution, and noise performance of our method. Our method uses less computation and is more robust than per-pixel depth estimation, making it appropriate for many applications, such as autorefocusing and autodenoising. [ABSTRACT FROM AUTHOR]