Light field imaging involves capturing both angular and spatial distribution of light; it enables new capabilities, such as post-capture digital refocusing, camera aperture adjustment, perspective shift, and depth estimation. Micro-lens array (MLA)-based light field cameras provide a cost-effective approach to light field imaging. There are two main limitations of MLA-based light field cameras: low spatial resolution and narrow baseline. While low spatial resolution limits the general purpose use and applicability of light field cameras, narrow baseline limits the depth estimation range and accuracy. In this paper, we present a hybrid stereo imaging system that includes a light field camera and a regular camera. The hybrid system addresses both spatial resolution and narrow baseline issues of the MLA-based light field cameras while preserving light field imaging capabilities.
Alternative to the standard MLA-based light field camera design @cite_14 , where the MLA is placed at the image plane of the main lens and the sensor is placed at the focal length of the lenslets, there is another design approach where the MLA is placed to relay image from the intermediate image plane of the main lens to the sensor @cite_29 . This design is known as focused plenoptic camera.'' As in the case of the standard light field camera approach, super-resolution restoration for focused plenoptic cameras is also possible @cite_24 .
@cite_24 @cite_29 @cite_14
2133515844 2095061755 2524177174 2082419056
Plenoptic cameras, constructed with internal microlens arrays, focus those microlenses at infinity in order to sample the 4D radiance directly at the microlenses. The consequent assumption is that each microlens image is completely defocused with respect to to the image created by the main camera lens and the outside object. As a result, only a single pixel in the final image can be rendered from it, resulting in disappointingly low resolution. In this paper, we present a new approach to lightfield capture and image rendering that interprets the microlens array as an imaging system focused on the focal plane of the main camera lens. This approach captures a lightfield with significantly higher spatial resolution than the traditional approach, allowing us to render high resolution images that meet the expectations of modern photographers. Although the new approach samples the lightfield with reduced angular density, analysis and experimental results demonstrate that there is sufficient parallax to completely support lightfield manipulation algorithms such as refocusing and novel views Autofocus algorithms are used to restore images in nonideal synthetic aperture radar imaging systems. In this paper, we propose a bilinear parametric model for the unknown image and the nuisance phase parameters and derive an efficient maximum-likelihood autofocus (MLA) algorithm. In the special case of a simple image model and a narrow range of look angles, MLA coincides with the successful multichannel autofocus (MCA). MLA can be interpreted as a generalization of MCA to a larger class of models with a larger range of look angles. We analyze its advantages over previous extensions of MCA in terms of identifiability conditions and noise sensitivity. As a byproduct, we also propose numerical approximations to the difficult constant modulus quadratic program that lies at the core of these algorithms. We demonstrate the superior performance of our proposed methods using computer simulations in both the correct and mismatched system models. MLA performs better than other methods, both in terms of the mean squared error and visual quality of the restored image. Plenoptic images are one type of light field contents produced by using a combination of a conventional camera and an additional optical component in the form of microlens arrays, which are positioned in front of the image sensor surface. This camera setup can capture a sub-sampling of the light field with high spatial fidelity over a small range, and with a more coarsely sampled angle range. The earliest applications that leverage on the plenoptic image content is image refocusing, non-linear distribution of out-of-focus areas, SNR vs. resolution trade-offs, and 3D-image creation. All functionalities are provided by using post-processing methods. In this work, we evaluate a compression method that we previously proposed for a different type of plenoptic image (focused or plenoptic camera 2.0 contents) than the unfocused or plenoptic camera 1.0 that is used in this Grand Challenge. The method is an extension of the state-of-the-art video compression standard HEVC where we have brought the capability of bi-directional inter-frame prediction into the spatial prediction. The method is evaluated according to the scheme set out by the Grand Challenge, and the results show a high compression efficiency compared with JPEG, i.e., up to 6 dB improvements for the tested images. Plenoptic cameras are a promising solution to increase the capabilities of current commercial cameras because they capture the four-dimensional lightfield of a scene. Processing the recorded lightfield, these cameras offer the possibility of focusing the scene after the shot or obtaining 3D information. Conventional photographs focused on determined planes can be obtained through projections of the four-dimensional lightfield onto two spatial dimensions. These photographs can be efficiently computed using the Fourier Slice technique but their resolution is usually less than 1 of the full resolution of the camera sensor. Several super-resolution methods have been recently developed to increase the spatial resolution of plenoptic cameras. In this paper we propose a new super-resolution method based on the Fourier Slice technique. We show how several existing super-resolution methods can be seen as particular cases of this approach. Besides the theoretical interest of this unified view, we also show how to obtain simultaneously spatial and depth super-resolution removing the limitations of previous approaches.