Eli Shechtman

PatchMatch: a randomized correspondence algorithm for structural image editing

This paper presents interactive image editing tools using a new randomized algorithm for quickly finding approximate nearest-neighbor matches between image patches. Previous research in graphics and vision has leveraged such nearest-neighbor searches to provide a variety of high-level digital image editing tools. However, the cost of computing a field of such matches for an entire image has eluded previous efforts to provide interactive performance. Our algorithm offers substantial performance improvements over the previous state of the art (20-100x), enabling its use in interactive editing tools. The key insights driving the algorithm are that some good patch matches can be found via random sampling, and that natural coherence in the imagery allows us to propagate such matches quickly to surrounding areas. We offer theoretical analysis of the convergence properties of the algorithm, as well as empirical and practical evidence for its high quality and performance. This one simple algorithm forms the basis for a variety of tools -- image retargeting, completion and reshuffling -- that can be used together in the context of a high-level image editing application. Finally, we propose additional intuitive constraints on the synthesis process that offer the user a level of control unavailable in previous methods.

The generalized patchmatch correspondence algorithm

PatchMatch is a fast algorithm for computing dense approximate nearest neighbor correspondences between patches of two image regions [1]. This paper generalizes PatchMatch in three ways: (1) to find k nearest neighbors, as opposed to just one, (2) to search across scales and rotations, in addition to just translations, and (3) to match using arbitrary descriptors and distances, not just sum-of-squared-differences on patch colors. In addition, we offer new search and parallelization strategies that further accelerate the method, and we show performance improvements over standard kd-tree techniques across a variety of inputs. In contrast to many previous matching algorithms, which for efficiency reasons have restricted matching to sparse interest points, or spatially proximate matches, our algorithm can efficiently find global, dense matches, even while matching across all scales and rotations. This is especially useful for computer vision applications, where our algorithm can be used as an efficient general-purpose component. We explore a variety of vision applications: denoising, finding forgeries by detecting cloned regions, symmetry detection, and object detection.

Non-rigid dense correspondence with applications for image enhancement

This paper presents a new efficient method for recovering reliable local sets of dense correspondences between two images with some shared content. Our method is designed for pairs of images depicting similar regions acquired by different cameras and lenses, under non-rigid transformations, under different lighting, and over different backgrounds. We utilize a new coarse-to-fine scheme in which nearest-neighbor field computations using Generalized PatchMatch [Barnes et al. 2010] are interleaved with fitting a global non-linear parametric color model and aggregating consistent matching regions using locally adaptive constraints. Compared to previous correspondence approaches, our method combines the best of two worlds: It is dense, like optical flow and stereo reconstruction methods, and it is also robust to geometric and photometric variations, like sparse feature matching. We demonstrate the usefulness of our method using three applications for automatic example-based photograph enhancement: adjusting the tonal characteristics of a source image to match a reference, transferring a known mask to a new image, and kernel estimation for image deblurring.

Image melding: combining inconsistent images using patch-based synthesis

Current methods for combining two different images produce visible artifacts when the sources have very different textures and structures. We present a new method for synthesizing a transition region between two source images, such that inconsistent color, texture, and structural properties all change gradually from one source to the other. We call this process image melding. Our method builds upon a patch-based optimization foundation with three key generalizations: First, we enrich the patch search space with additional geometric and photometric transformations. Second, we integrate image gradients into the patch representation and replace the usual color averaging with a screened Poisson equation solver. And third, we propose a new energy based on mixed L2/L0 norms for colors and gradients that produces a gradual transition between sources without sacrificing texture sharpness. Together, all three generalizations enable patch-based solutions to a broad class of image melding problems involving inconsistent sources: object cloning, stitching challenging panoramas, hole filling from multiple photos, and image harmonization. In several cases, our unified method outperforms previous state-of-the-art methods specifically designed for those applications.

Robust patch-based hdr reconstruction of dynamic scenes

High dynamic range (HDR) imaging from a set of sequential exposures is an easy way to capture high-quality images of static scenes, but suffers from artifacts for scenes with significant motion. In this paper, we propose a new approach to HDR reconstruction that draws information from all the exposures but is more robust to camera/scene motion than previous techniques. Our algorithm is based on a novel patch-based energy-minimization formulation that integrates alignment and reconstruction in a joint optimization through an equation we call the HDR image synthesis equation. This allows us to produce an HDR result that is aligned to one of the exposures yet contains information from all of them. We present results that show considerable improvement over previous approaches.

Expression flow for 3D-aware face component transfer

We address the problem of correcting an undesirable expression on a face photo by transferring local facial components, such as a smiling mouth, from another face photo of the same person which has the desired expression. Direct copying and blending using existing compositing tools results in semantically unnatural composites, since expression is a global effect and the local component in one expression is often incompatible with the shape and other components of the face in another expression. To solve this problem we present Expression Flow, a 2D flow field which can warp the target face globally in a natural way, so that the warped face is compatible with the new facial component to be copied over. To do this, starting with the two input face photos, we jointly construct a pair of 3D face shapes with the same identity but different expressions. The expression flow is computed by projecting the difference between the two 3D shapes back to 2D. It describes how to warp the target face photo to match the expression of the reference photo. User studies suggest that our system is able to generate face composites with much higher fidelity than existing methods.

Video tapestries with continuous temporal zoom

We present a novel approach for summarizing video in the form of a multiscale image that is continuous in both the spatial domain and across the scale dimension: There are no hard borders between discrete moments in time, and a user can zoom smoothly into the image to reveal additional temporal details. We call these artifacts tapestries because their continuous nature is akin to medieval tapestries and other narrative depictions predating the advent of motion pictures. We propose a set of criteria for such a summarization, and a series of optimizations motivated by these criteria. These can be performed as an entirely offline computation to produce high quality renderings, or by adjusting some optimization parameters the later stages can be solved in real time, enabling an interactive interface for video navigation. Our video tapestries combine the best aspects of two common visualizations, providing the visual clarity of DVD chapter menus with the information density and multiple scales of a video editing timeline representation. In addition, they provide continuous transitions between zoom levels. In a user study, participants preferred both the aesthetics and efficiency of tapestries over other interfaces for visual browsing.

Exploring photobios

We present an approach for generating face animations from large image collections of the same person. Such collections, which we call photobios, sample the appearance of a person over changes in pose, facial expression, hairstyle, age, and other variations. By optimizing the order in which images are displayed and cross-dissolving between them, we control the motion through face space and create compelling animations (e.g., render a smooth transition from frowning to smiling). Used in this context, the cross dissolve produces a very strong motion effect; a key contribution of the paper is to explain this effect and analyze its operating range. The approach operates by creating a graph with faces as nodes, and similarities as edges, and solving for walks and shortest paths on this graph. The processing pipeline involves face detection, locating fiducials (eyes/nose/mouth), solving for pose, warping to frontal views, and image comparison based on Local Binary Patterns. We demonstrate results on a variety of datasets including time-lapse photography, personal photo collections, and images of celebrities downloaded from the Internet. Our approach is the basis for the Face Movies feature in Googles Picasa.

Patch-based high dynamic range video

Despite significant progress in high dynamic range (HDR) imaging over the years, it is still difficult to capture high-quality HDR video with a conventional, off-the-shelf camera. The most practical way to do this is to capture alternating exposures for every LDR frame and then use an alignment method based on optical flow to register the exposures together. However, this results in objectionable artifacts whenever there is complex motion and optical flow fails. To address this problem, we propose a new approach for HDR reconstruction from alternating exposure video sequences that combines the advantages of optical flow and recently introduced patch-based synthesis for HDR images. We use patch-based synthesis to enforce similarity between adjacent frames, increasing temporal continuity. To synthesize visually plausible solutions, we enforce constraints from motion estimation coupled with a search window map that guides the patch-based synthesis. This results in a novel reconstruction algorithm that can produce high-quality HDR videos with a standard camera. Furthermore, our method is able to synthesize plausible texture and motion in fast-moving regions, where either patch-based synthesis or optical flow alone would exhibit artifacts. We present results of our reconstructed HDR video sequences that are superior to those produced by current approaches.

Cosaliency: where people look when comparing images

Image triage is a common task in digital photography. Determining which photos are worth processing for sharing with friends and family and which should be deleted to make room for new ones can be a challenge, especially on a device with a small screen like a mobile phone or camera. In this work we explore the importance of local structure changes?e.g. human pose, appearance changes, object orientation, etc.?to the photographic triage task. We perform a user study in which subjects are asked to mark regions of image pairs most useful in making triage decisions. From this data, we train a model for image saliency in the context of other images that we call cosaliency. This allows us to create collection-aware crops that can augment the information provided by existing thumbnailing techniques for the image triage task.