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.
All single-sensor light field imaging systems are fundamentally limited by the spatial-angular resolution trade-off, and the above-mentioned restoration methods have performance limitations in addition to the computational costs. Another approach for improving spatial resolution is to use a hybrid two camera system, including a light field camera and a high-resolution camera, and merge the images to improve spatial resolution @cite_8 @cite_5 @cite_38 . Dictionary-learning based techniques are adopted @cite_8 @cite_5 in this problem as well: High-resolution image patches from the regular camera are extracted and stored as a high-resolution patch dictionary. These high-resolution patches are downsampled; and from the downsampled patches, low-resolution features are extracted to form a low-resolution patch dictionary. During super-resolution reconstruction, a low-resolution image patch is enhanced through determining (based on feature matching) and using the corresponding high-resolution patches in the dictionary. In @cite_38 , high-resolution image is decomposed with complex steerable pyramid filters; the depth map from the light field is upsampled using joint bilateral upsampling; perspective shift amounts are estimated from the upsampled depth map, and these shift amounts are used to modify the phase of the decomposed high-resolution image; with the modified phases, pyramid reconstruction is applied to obtain high-resolution light field.
@cite_5 @cite_38 @cite_8
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Recently, light field cameras have drawn much attraction for their innovative performance in photographic and scientific applications. However, narrow baselines and constrained spatial resolution of current light field cameras impose restrictions on their usability. Therefore, we design a hybrid imaging system containing a light field camera and a high-resolution digital single lens reflex camera, and these two kinds of cameras share the same optical path with a beam splitter so as to achieve the reconstruction of high-resolution light fields. The high-resolution 4D light fields are reconstructed with a phase-based perspective variation strategy. First, we apply complex steerable pyramid decomposition on the high-resolution image from the digital single lens reflex camera. Then, we perform phase-based perspective-shift processing with the disparity value, which is extracted from the upsampled light field depth map, to create high-resolution synthetic light field images. High-resolution digital refocused images and high-resolution depth maps can be generated in this way. Furthermore, controlling the magnitude of the perspective shift enables us to change the depth of field rendering in the digital refocused images. We show several experimental results to demonstrate the effectiveness of our approach. The low spatial resolution of light-field image poses significant difficulties in exploiting its advantage. To mitigate the dependency of accurate depth or disparity information as priors for light-field image super-resolution, we propose an implicitly multi-scale fusion scheme to accumulate contextual information from multiple scales for super-resolution reconstruction. The implicitly multi-scale fusion scheme is then incorporated into bidirectional recurrent convolutional neural network, which aims to iteratively model spatial relations between horizontally or vertically adjacent sub-aperture images of light-field data. Within the network, the recurrent convolutions are modified to be more effective and flexible in modeling the spatial correlations between neighboring views. A horizontal sub-network and a vertical sub-network of the same network structure are ensembled for final outputs via stacked generalization. Experimental results on synthetic and real-world data sets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in peak signal-to-noise ratio and gray-scale structural similarity indexes, which also achieves superior quality for human visual systems. Furthermore, the proposed method can enhance the performance of light field applications such as depth estimation. Portable light field (LF) cameras have demonstrated capabilities beyond conventional cameras. In a single snapshot, they enable digital image refocusing and 3D reconstruction. We show that they obtain a larger depth of field but maintain the ability to reconstruct detail at high resolution. In fact, all depths are approximately focused, except for a thin slab where blur size is bounded, i.e., their depth of field is essentially inverted compared to regular cameras. Crucial to their success is the way they sample the LF, trading off spatial versus angular resolution, and how aliasing affects the LF. We show that applying traditional multiview stereo methods to the extracted low-resolution views can result in reconstruction errors due to aliasing. We address these challenges using an explicit image formation model, and incorporate Lambertian and texture preserving priors to reconstruct both scene depth and its superresolved texture in a variational Bayesian framework, eliminating aliasing by fusing multiview information. We demonstrate the method on synthetic and real images captured with our LF camera, and show that it can outperform other computational camera systems. In this paper, we explore the structure of light field (LF) and efficiently improve the performance of the pseudo sequence based lenslet image compression, by optimized sub-view rearrangement, enhanced illumination compensation and adaptive reconstruction filtering. First, the decomposed sub-view images are rearranged into a pseudo sequence according to our optimized scan order based on sub-view correlation. Second, the generated pseudo sequence is compressed with JEM codec in which we enhance the illumination compensation by adaptively selecting reference pixels used in parameter derivation. Finally, to reduce the distortions in lenslet image decomposition and reconstruction, we propose an lenslet reconstruction method by applying adaptive filters to the reconstructed lenslet images to compensate the reconstruction errors. Each filter is derived by minimizing the distortions between the original and reconstructed pixels with same geolocation in the lenslet image. Extensive experimental results show that the proposed method achieves up to 53.7 bit rate reduction over HEVC intra coding and 34.8 over JEM intra coding in terms of BDBR.