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.
On low spatial resolution: There are various methods proposed to address the low spatial resolution issue in MLA-based light field cameras. One main approach is to apply super-resolution image restoration to light field sub-aperture images. Super-resolution in a Bayesian framework is commonly used, for example, in @cite_10 with Lambertian and textural priors, in @cite_18 with a Gaussian mixture model, and in @cite_41 with a variational formulation. Learning-based methods are adopted as well, including dictionary-based learning @cite_21 and deep convolutional neural networks @cite_30 @cite_37 . In addition to spatial domain super-resolution restoration, Fourier-domain techniques @cite_35 @cite_12 and wave optics based 3D deconvolution methods @cite_31 @cite_39 @cite_6 @cite_40 have also been utilized.
@cite_30 @cite_35 @cite_18 @cite_37 @cite_41 @cite_21 @cite_6 @cite_39 @cite_40 @cite_31 @cite_10 @cite_12
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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. Image Super-Resolution: Historical Overview and Future Challenges, J. Yang and T. Huang Introduction to Super-Resolution Notations Techniques for Super-Resolution Challenge issues for Super-Resolution Super-Resolution Using Adaptive Wiener Filters, R.C. Hardie Introduction Observation Model AWF SR Algorithms Experimental Results Conclusions Acknowledgments Locally Adaptive Kernel Regression for Space-Time Super-Resolution, H. Takeda and P. Milanfar Introduction Adaptive Kernel Regression Examples Conclusion AppendiX Super-Resolution With Probabilistic Motion Estimation, M. Protter and M. Elad Introduction Classic Super-Resolution: Background The Proposed Algorithm Experimental Validation Summary Spatially Adaptive Filtering as Regularization in Inverse Imaging, A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian Introduction Iterative filtering as regularization Compressed sensing Super-resolution Conclusions Registration for Super-Resolution, P. Vandewalle, L. Sbaiz, and M. Vetterli Camera Model What Is Resolution? Super-Resolution as a Multichannel Sampling Problem Registration of Totally Aliased Signals Registration of Partially Aliased Signals Conclusions Towards Super-Resolution in the Presence of Spatially Varying Blur, M. Sorel, F. Sroubek and J. Flusser Introduction Defocus and Optical Aberrations Camera Motion Blur Scene Motion Algorithms Conclusion Acknowledgments Toward Robust Reconstruction-Based Super-Resolution, M. Tanaka and M. Okutomi Introduction Overviews Robust SR Reconstruction with Pixel Selection Robust Super-Resolution Using MPEG Motion Vectors Robust Registration for Super-Resolution Conclusions Multi-Frame Super-Resolution from a Bayesian Perspective, L. Pickup, S. Roberts, A. Zisserman and D. Capel The Generative Model Where Super-Resolution Algorithms Go Wrong Simultaneous Super-Resolution Bayesian Marginalization Concluding Remarks Variational Bayesian Super Resolution Reconstruction, S. Derin Babacan, R. Molina, and A.K. Katsaggelos Introduction Problem Formulation Bayesian Framework for Super Resolution Bayesian Inference Variational Bayesian Inference Using TV Image Priors Experiments Estimation of Motion and Blur Conclusions Acknowledgements Pattern Recognition Techniques for Image Super-Resolution, K. Ni and T.Q. Nguyen Introduction Nearest Neighbor Super-Resolution Markov Random Fields and Approximations Kernel Machines for Image Super-Resolution Multiple Learners and Multiple Regressions Design Considerations and Examples Remarks Glossary Super-Resolution Reconstruction of Multi-Channel Images, O.G. Sezer and Y. Altunbasak Introduction Notation Image Acquisition Model Subspace Representation Reconstruction Algorithm Experiments & Discussions Conclusion New Applications of Super-Resolution in Medical Imaging, M.D.Robinson, S.J. Chiu, C.A. Toth, J.A. Izatt, J.Y. Lo, and S. Farsiu Introduction The Super-Resolution Framework New Medical Imaging Applications Conclusion Acknowledgment Practicing Super-Resolution: What Have We Learned? N. Bozinovic Abstract Introduction MotionDSP: History and Concepts Markets and Applications Technology Results Lessons Learned Conclusions 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. Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inaccurately, as often happens for nonglobal motion fields, annoying artifacts appear in the super-resolved outcome. Encouraged by recent developments on the video denoising problem, where state-of-the-art algorithms are formed with no explicit motion estimation, we seek a super-resolution algorithm of similar nature that will allow processing sequences with general motion patterns. In this paper, we base our solution on the Nonlocal-Means (NLM) algorithm. We show how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation. Results on several test movies show that the proposed method is very successful in providing super-resolution on general sequences. 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. Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually. The conventional methods for estimating camera poses and scene structures from severely blurry or low resolution images often result in failure. The off-the-shelf deblurring or super-resolution methods may show visually pleasing results. However, applying each technique independently before matching is generally unprofitable because this naive series of procedures ignores the consistency between images. In this paper, we propose a pioneering unified framework that solves four problems simultaneously, namely, dense depth reconstruction, camera pose estimation, super-resolution, and deblurring. By reflecting a physical imaging process, we formulate a cost minimization problem and solve it using an alternating optimization technique. The experimental results on both synthetic and real videos show high-quality depth maps derived from severely degraded images that contrast the failures of naive multi-view stereo methods. Our proposed method also produces outstanding deblurred and super-resolved images unlike the independent application or combination of conventional video deblurring, super-resolution methods. For the instrument limitation and imperfect imaging optics, it is difficult to acquire high spatial resolution hyperspectral imagery. Low spatial resolution will result in a lot of mixed pixels and greatly degrade the detection and recognition performance, affect the related application in civil and military fields. As a powerful statistical image modeling technique, sparse representation can be utilized to analyze the hyperspectral image efficiently. Hyperspectral imagery is intrinsically sparse in spatial and spectral domains, and image super-resolution quality largely depends on whether the prior knowledge is utilized properly. In this article, we propose a novel hyperspectral imagery super-resolution method by utilizing the sparse representation and spectral mixing model. Based on the sparse representation model and hyperspectral image acquisition process model, small patches of hyperspectral observations from different wavelengths can be represented as weighted linear combinations of a small number of atoms in pre-trained dictionary. Then super-resolution is treated as a least squares problem with sparse constraints. To maintain the spectral consistency, we further introduce an adaptive regularization terms into the sparse representation framework by combining the linear spectrum mixing model. Extensive experiments validate that the proposed method achieves much better results. With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. We show the performance of our approach using only four corner sub-aperture views from the light fields captured by the Lytro Illum camera. Experimental results show that our approach synthesizes high-quality images that are superior to the state-of-the-art techniques on a variety of challenging real-world scenes. We believe our method could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase. Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method. Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method. We demonstrate that sub-wavelength optical images borne on partially-spatially-incoherent light can be recovered, from their far-field or from the blurred image, given the prior knowledge that the image is sparse, and only that. The reconstruction method relies on the recently demonstrated sparsity-based sub-wavelength imaging. However, for partially-spatially-incoherent light, the relation between the measurements and the image is quadratic, yielding non-convex measurement equations that do not conform to previously used techniques. Consequently, we demonstrate new algorithmic methodology, referred to as quadratic compressed sensing, which can be applied to a range of other problems involving information recovery from partial correlation measurements, including when the correlation function has local dependencies. Specifically for microscopy, this method can be readily extended to white light microscopes with the additional knowledge of the light source spectrum.