Light-field cameras are quickly becoming commodity items, with consumer and industrial applications. They capture many nearby views simultaneously using a single image with a micro-lens array, thereby providing a wealth of cues for depth recovery: defocus, correspondence, and shading. In particular, apart from conventional image shading, one can refocus images after acquisition, and shift one's viewpoint within the sub-apertures of the main lens, effectively obtaining multiple views. We present a principled algorithm for dense depth estimation that combines defocus and correspondence metrics. We then extend our analysis to the additional cue of shading, using it to refine fine details in the shape. By exploiting an all-in-focus image, in which pixels are expected to exhibit angular coherence, we define an optimization framework that integrates photo consistency, depth consistency, and shading consistency. We show that combining all three sources of information: defocus, correspondence, and shading, outperforms state-of-the-art light-field depth estimation algorithms in multiple scenarios.

Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current light-field depth estimation methods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. The standard Lambertian photoconsistency measure considers the variance of different views, effectively enforcing point-consistency, i.e., that all views map to the same point in RGB space. This variance or point-consistency condition is a poor metric for glossy surfaces. In this paper, we present a novel theory of the relationship between light-field data and reflectance from the dichromatic model. We present a physically-based and practical method to estimate the light source color and separate specularity. We present a new photo consistency metric, line-consistency, which represents how viewpoint changes affect specular points. We then show how the new metric can be used in combination with the standard Lambertian variance or point-consistency measure to give us results that are robust against scenes with glossy surfaces. With our analysis, we can also robustly estimate multiple light source colors and remove the specular component from glossy objects. We show that our method outperforms current state-of-the-art specular removal and depth estimation algorithms in multiple real world scenarios using the consumer Lytro and Lytro Illum light field cameras.

Light-field cameras are now used in consumer and industrial applications. Recent papers and products have demonstrated practical depth recovery algorithms from a passive single-shot capture. However, current light-field capture devices have narrow baselines and constrained spatial resolution; therefore, the accuracy of depth recovery is limited, requiring heavy regularization and producing planar depths that do not resemble the actual geometry. Using shading information is essential to improve the shape estimation. We develop an improved technique for local shape estimation from defocus and correspondence cues, and show how shading can be used to further refine the depth. Light-field cameras are able to capture both spatial and angular data, suitable for refocusing. By locally refocusing each spatial pixel to its respective estimated depth, we produce an all-in-focus image where all viewpoints converge onto a point in the scene. Therefore, the angular pixels have angular coherence, which exhibits three properties: photo consistency, depth consistency, and shading consistency. We propose a new framework that uses angular coherence to optimize depth and shading. The optimization framework estimates both general lighting in natural scenes and shading to improve depth regularization. Our method outperforms current state-of-the-art light-field depth estimation algorithms in multiple scenarios, including real images.

2D spatial image windows are used for comparing pixel values in computer vision applications such as correspondence for optical flow and 3D reconstruction, bilateral filtering, and image segmentation. However, pixel window comparisons can suffer from varying defocus blur and perspective at different depths, and can also lead to a loss of precision. In this paper, we leverage the recent use of light-field cameras to propose alternative oriented light-field windows that enable more robust and accurate pixel comparisons. For Lambertian surfaces focused to the correct depth, the 2D distribution of angular rays from a pixel remains consistent. We build on this idea to develop an oriented 4D light-field window that accounts for shearing (depth), translation (matching), and windowing. Our main application is to scene flow, a generalization of optical flow to the 3D vector field describing the motion of each point in the scene. We show significant benefits of oriented light-field windows over standard 2D spatial windows. We also demonstrate additional applications of oriented light-field windows for bilateral filtering and image segmentation.

Light-field cameras have recently become available to the consumer market. An array of micro-lenses captures enough information that one can refocus images after acquisition, as well as shift one's viewpoint within the sub-apertures of the main lens, effectively obtaining multiple views. Thus, depth cues from both defocus and correspondence are available simultaneously in a single capture. Previously, defocus could be achieved only through multiple image exposures focused at different depths, while correspondence cues needed multiple exposures at different viewpoints or multiple cameras, moreover, both cues could not easily be obtained together. In this paper, we present a novel simple and principled algorithm that computes dense depth estimation by combining both defocus and correspondence depth cues. We analyze the x-u 2D epipolar image (EPI), where by convention we assume the spatial x coordinate is horizontal and the angular u coordinate is vertical (our final algorithm uses the full 4D EPI). We show that defocus depth cues are obtained by computing the horizontal (spatial) variance after vertical (angular) integration, and correspondence depth cues by computing the vertical (angular) variance. We then show how to combine the two cues into a high quality depth map, suitable for computer vision applications such as matting, full control of depth-of-field, and surface reconstruction.

Abstract Focus misses are common in image capture, such as when the camera or the subject moves rapidly in sports and macro photography. One option to sharpen focus‐missed photographs is through single image deconvolution, but high‐frequency data cannot be fully recovered; therefore, artifacts such as ringing and amplified noise become apparent. We propose a new method that uses assisting, similar but different, sharp image(s) provided by the user (such as multiple images of the same subject in different positions captured using a burst of photographs). Our first contribution is to theoretically analyze the errors in three sources of data—a slightly sharpened original input image that we call the target, single image deconvolution with an aggressive inverse filter, and warped assisting image(s) registered using optical flow. We show that these three sources have different error characteristics, depending on image location and frequency band (for example, aggressive deconvolution is more accurate in high‐frequency regions like edges). Next, we describe a practical method to compute these errors, given we have no ground truth and cannot easily work in the Fourier domain. Finally, we select the best source of data for a given pixel and scale in the Laplacian pyramid. We accurately transfer high‐frequency data to the input, while minimizing artifacts. We demonstrate sharpened results on out‐of‐focus images in macro, sports, portrait and wildlife photography.

Abstract Optical flow is a critical component of video editing applications, e.g. for tasks such as object tracking, segmentation, and selection. In this paper, we propose an optical flow algorithm calledSimpleFlowwhose running times increase sublinearly in the number of pixels. Central to our approach is a probabilistic representation of the motion flow that is computed using only local evidence and without resorting to global optimization. To estimate the flow in image regions where the motion is smooth, we use a sparse set of samples only, thereby avoiding the expensive computation inherent in traditional dense algorithms. We show that our results can be used as is for a variety of video editing tasks. For applications where accuracy is paramount, we use our result to bootstrap a global optimization. This significantly reduces the running times of such methods without sacrificing accuracy. We also demonstrate that the SimpleFlow algorithm can process HD and 4K footage in reasonable times.