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. Current light field (LF) cameras provide low spatial resolution and limited depth-of-field (DOF) control when compared to traditional digital SLR (DSLR) cameras. We show that a hybrid imaging system consisting of a standard LF camera and a high-resolution standard camera enables (a) achieve high-resolution digital refocusing, (b) better DOF control than LF cameras, and (c) render graceful high-resolution viewpoint variations, all of which were previously unachievable. We propose a simple patch-based algorithm to super-resolve the low-resolution views of the light field using the high-resolution patches captured using a high-resolution SLR camera. The algorithm does not require the LF camera and the DSLR to be co-located or for any calibration information regarding the two imaging systems. We build an example prototype using a Lytro camera (380×380 pixel spatial resolution) and a 18 megapixel (MP) Canon DSLR camera to generate a light field with 11 MP resolution (9× super-resolution) and about 1 over 9 th of the DOF of the Lytro camera. We show several experimental results on challenging scenes containing occlusions, specularities and complex non-lambertian materials, demonstrating the effectiveness of our approach. We present an algorithm to synthetically increase the resolution of a solitary depth image using only a generic database of local patches. Modern range sensors measure depths with non-Gaussian noise and at lower starting resolutions than typical visible-light cameras. While patch based approaches for upsampling intensity images continue to improve, this is the first exploration of patching for depth images. We match against the height field of each low resolution input depth patch, and search our database for a list of appropriate high resolution candidate patches. Selecting the right candidate at each location in the depth image is then posed as a Markov random field labeling problem. Our experiments also show how important further depth-specific processing, such as noise removal and correct patch normalization, dramatically improves our results. Perhaps surprisingly, even better results are achieved on a variety of real test scenes by providing our algorithm with only synthetic training depth data. This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low-resolution and high-resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low-resolution image patch can be applied with the high-resolution image patch dictionary to generate a high-resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs , reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution (SR) and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle SR with noisy inputs in a more unified framework. We present a simple guided super-resolution technique for increasing directional resolution without reliance on depth estimation or image correspondences. Rather, it searches for best- matching multidimensional (4D or 3D) patches within the entire captured data set to compose new directional images that are consistent in both the spatial and the directional domains. We describe algorithms for guided upsampling, iterative guided upsampling, and sampling code estimation. Our experimental results reveal that the outcomes of existing light-field camera arrays and light-stage systems can be improved without additional hardware requirements or recording effort simply by realignment of cameras or light sources to change their sampling patterns. We present a hybrid camera that combines the advantages of a high resolution camera and a high speed camera. Our hybrid camera consists of a pair of low-resolution high-speed (LRHS) cameras and a single high-resolution low-speed (HRLS) camera. The LRHS cameras are able to capture fast-motion with little motion blur. They also form a stereo pair and provide a low-resolution depth map. The HRLS camera provides a high spatial resolution but also introduces severe motion blur when capturing fast moving objects. We develop efficient algorithms to simultaneously motion-deblur the HRLS image and reconstruct a high resolution depth map. Our method estimates the motion flow in the LRHS pair and then warps the flow field to the HRLS camera to estimate the point spread function (PSF). We then deblur the HRLS image and use the resulting image to enhance the low-resolution depth map using joint bilateral filters. We demonstrate the hybrid camera in depth map super-resolution and motion deblurring with spatially varying kernels. Experiments show that our framework is robust and highly effective. We propose a super-resolution method that exploits selfsimilarities and group structural information of image patches using only one single input frame. The super-resolution problem is posed as learning the mapping between pairs of low-resolution and high-resolution image patches. Instead of relying on an extrinsic set of training images as often required in example-based super-resolution algorithms, we employ a method that generates image pairs directly from the image pyramid of one single frame. The generated patch pairs are clustered for training a dictionary by enforcing group sparsity constraints underlying the image patches. Super-resolution images are then constructed using the learned dictionary. Experimental results show the proposed method is able to achieve the state-of-the-art performance. This paper addresses the problem of generating a super-resolution (SR) image from a single low-resolution input image. We approach this problem from the perspective of compressed sensing. The low-resolution image is viewed as downsampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signal-atoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the downsampled signal. We will demonstrate the effectiveness of sparsity as a prior for regularizing the otherwise ill-posed super-resolution problem. We further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high-resolution image is competitive or even superior in quality to images produced by other SR methods. We propose a novel light field super-resolution framework based on hybrid imaging system, which combines two different imaging mechanisms: conventional imaging and current art-of-the-state imaging — light field imaging. We take advantage of conventional imaging in spatial resolution to make up light field and reconstruct a higher quality light field. In our method, we classify the points of the 3D scene: First, for highlight and occlusion, dictionary learning based interpolation is utilized, Second, for other areas, an improved patch matching algorithm is applied. As shown in experimental results, compared with four methods, which include the art-of-the-state algorithms, our approach is effective. Cameras face a fundamental tradeoff between the spatial and temporal resolution - digital still cameras can capture images with high spatial resolution, but most high-speed video cameras suffer from low spatial resolution. It is hard to overcome this tradeoff without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing and reconstructing the space-time volume in order to overcome this tradeoff. Our approach has two important distinctions compared to previous works: (1) we achieve sparse representation of videos by learning an over-complete dictionary on video patches, and (2) we adhere to practical constraints on sampling scheme which is imposed by architectures of present image sensor devices. Consequently, our sampling scheme can be implemented on image sensors by making a straightforward modification to the control unit. To demonstrate the power of our approach, we have implemented a prototype imaging system with per-pixel coded exposure control using a liquid crystal on silicon (LCoS) device. Using both simulations and experiments on a wide range of scenes, we show that our method can effectively reconstruct a video from a single image maintaining high spatial resolution. In this paper, we propose a novel coupled dictionary training method for single-image super-resolution (SR) based on patchwise sparse recovery, where the learned couple dictionaries relate the low- and high-resolution (HR) image patch spaces via sparse representation. The learning process enforces that the sparse representation of a low-resolution (LR) image patch in terms of the LR dictionary can well reconstruct its underlying HR image patch with the dictionary in the high-resolution image patch space. We model the learning problem as a bilevel optimization problem, where the optimization includes an l1-norm minimization problem in its constraints. Implicit differentiation is employed to calculate the desired gradient for stochastic gradient descent. We demonstrate that our coupled dictionary learning method can outperform the existing joint dictionary training method both quantitatively and qualitatively. Furthermore, for real applications, we speed up the algorithm approximately 10 times by learning a neural network model for fast sparse inference and selectively processing only those visually salient regions. Extensive experimental comparisons with state-of-the-art SR algorithms validate the effectiveness of our proposed approach. We address single image super-resolution using a statistical prediction model based on sparse representations of low- and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low- and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution. A novel face hallucination method is proposed in this paper for the reconstruction of a high-resolution face image from a low-resolution observation based on a set of high- and low-resolution training image pairs. Different from most of the established methods based on probabilistic or manifold learning models, the proposed method hallucinates the high-resolution image patch using the same position image patches of each training image. The optimal weights of the training image position-patches are estimated and the hallucinated patches are reconstructed using the same weights. The final high-resolution facial image is formed by integrating the hallucinated patches. The necessity of two-step framework or residue compensation and the differences between hallucination based on patch and global image are discussed. Experiments show that the proposed method without residue compensation generates higher-quality images and costs less computational time than some recent face image super-resolution (hallucination) techniques. Commercial Light-Field cameras provide spatial and angular information, but its limited resolution becomes an important problem in practical use. In this paper, we present a novel method for Light-Field image super-resolution (SR) via a deep convolutional neural network. Rather than the conventional optimization framework, we adopt a datadriven learning method to simultaneously up-sample the angular resolution as well as the spatial resolution of a Light-Field image. We first augment the spatial resolution of each sub-aperture image to enhance details by a spatial SR network. Then, novel views between the sub-aperture images are generated by an angular super-resolution network. These networks are trained independently but finally finetuned via end-to-end training. The proposed method shows the state-of-the-art performance on HCI synthetic dataset, and is further evaluated by challenging real-world applications including refocusing and depth map estimation. Light-field cameras can capture spatial and angular information of light with a single exposure, but they suffer from low spatial resolution that limits their performance in practical use. Superresolution (SR) methods have been used to improve spatial resolution of light-field images, but most of them do not take full advantages of the particular structure of the light field. In this letter, we present an SR method to obtain light-field images with high quality and geometric consistency via a combined deep convolutional neural networks (CNNs) framework. The spatial resolution of subaperture images is enhanced separately by a single-image superresolution deep CNN. Then, an epipolar plane image enhancement deep CNN is proposed to restore the geometric consistency of these images. Experimental results show that our method achieves the state-of-the-art performance on both quantitative and qualitative evaluations. In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold teaming methods, particularly locally linear embedding (LLE). Specifically, small image patches in the lowand high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is very flexible and gives good empirical results. 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. A new generation of active 3D range sensors, such as time-of-flight cameras, enables recording of full-frame depth maps at video frame rate. Unfortunately, the captured data are typically starkly contaminated by noise and the sensors feature only a rather limited image resolution. We therefore present a pipeline to enhance the quality and increase the spatial resolution of range data in real-time by upsampling the range information with the data from a high resolution video camera. Our algorithm is an adaptive multi-lateral upsampling filter that takes into account the inherent noisy nature of real-time depth data. Thus, we can greatly improve reconstruction quality, boost the resolution of the data to that of the video sensor, and prevent unwanted artifacts like texture copy into geometry. Our technique has been crafted to achieve improvement in depth map quality while maintaining high computational efficiency for a real-time application. By implementing our approach on the GPU, the creation of a real-time 3D camera with video camera resolution is feasible. This paper describes a single-image super-resolution (SR) algorithm based on nonnegative neighbor embedding. It belongs to the family of single-image example-based SR algorithms, since it uses a dictionary of low resolution (LR) and high resolution (HR) trained patch pairs to infer the unknown HR details. Each LR feature vector in the input image is expressed as the weighted combination of its K nearest neighbors in the dictionary; the corresponding HR feature vector is reconstructed under the assumption that the local LR embedding is preserved. Three key aspects are introduced in order to build a low-complexity competitive algorithm: (i) a compact but efficient representation of the patches (feature representation) (ii) an accurate estimation of the patches by their nearest neighbors (weight computation) (iii) a compact and already built (therefore external) dictionary, which allows a one-step upscaling. The neighbor embedding SR algorithm so designed is shown to give good visual results, comparable to other state-of-the-art methods, while presenting an appreciable reduction of the computational time. This paper introduces a 3D imaging framework that combines high-resolution photometric stereo and low-resolution depth. Our approach targets imaging scenarios based on either macro-lens photography combined with focal stacking or a large-format camera that are able to image objects with more than 600 samples per mm @math . These imaging techniques allow photometric stereo algorithms to obtain surface normals at resolutions that far surpass corresponding depth values obtained with traditional approaches such as structured-light, passive stereo, or depth-from-focus. Our work offers two contributions for 3D imaging based on these scenarios. The first is a multi-resolution, patched-based surface reconstruction scheme that can robustly handle the significant resolution difference between our surface normals and depth samples. The second is a method to improve the initial normal estimation by using all the available focal information for images obtained using a focal stacking technique.