ScribbleBoost: Adding Classification to Edge‐Aware Interpolation of Local Image and Video Adjustments

One of the most common tasks in image and video editing is the local adjustment of various properties (e.g., saturation or brightness) of regions within an image or video. Edge‐aware interpolation of user‐drawn scribbles offers a less effort‐intensive approach to this problem than traditional region selection and matting. However, the technique suffers a number of limitations, such as reduced performance in the presence of texture contrast, and the inability to handle fragmented appearances. We significantly improve the performance of edge‐aware interpolation for this problem by adding a boosting‐based classification step that learns to discriminate between the appearance of scribbled pixels. We show that this novel data term in combination with an existing edge‐aware optimization technique achieves substantially better results for the local image and video adjustment problem than edge‐aware interpolation techniques without classification, or related methods such as matting techniques or graph cut segmentation.     

Contrast Restoration by Adaptive Countershading

The ABSTRACT is to be in fully-justified italicized text, between two horizontal lines, in one-column format, below the author and affiliation information. Use the word “Abstract” as the title, in 9-point Times, boldface type, left-aligned to the text, initially capitalized. The abstract is to be in 9-point, single-spaced type. The abstract may be up to 3 inches (7.62 cm) long. Leave one blank line after the abstract, then add the subject categories according to the ACM Classification Index (see http://www.acm.org/class/1998/ ).     

Viewfinder Alignment

The viewfinder of a digital camera has traditionally been used for one purpose: to display to the user a preview of what is seen through the camera's lens. High quality cameras are now available on devices such as mobile phones and PDAs, which provide a platform where the camera is a programmable device, enabling applications such as online computational photography, computer vision‐based interactive gaming, and augmented reality. For such online applications, the camera viewfinder provides the user's main interaction with the environment. In this paper, we describe an algorithm for aligning successive viewfinder frames. First, an estimate of inter‐frame translation is computed by aligning integral projections of edges in two images. The estimate is then refined to compute a full 2D similarity transformation by aligning point features. Our algorithm is robust to noise, never requires storing more than one viewfinder frame in memory, and runs at 30 frames per second on standard smartphone hardware. We use viewfinder alignment for panorama capture, low‐light photography, and a camera‐based game controller.     

An Efficient and Scalable Image Filtering Framework Using VIPS Fusion

Edge‐preserving image filtering is a valuable tool for a variety of applications in image processing and computer vision. Motivated by a new simple but effective local Laplacian filter, we propose a scalable and efficient image filtering framework to extend this edge‐preserving image filter and construct an uniform implementation in O (N) time. The proposed framework is built upon a practical global‐to‐local strategy. The input image is first remapped globally by a series of tentative remapping functions to generate a virtual candidate image sequence (Virtual Image Pyramid Sequence, VIPS). This sequence is then recombined locally to a single output image by a flexible edge‐aware pixel‐level fusion rule. To avoid halo artifacts, both the output image and the virtual candidate image sequence are transformed into multi‐resolution pyramid representations. Four examples, single image dehazing, multi‐exposure fusion, fast edge‐preserving filtering and tone‐mapping, are presented as the concrete applications of the proposed framework. Experiments on filtering effect and computational efficiency indicate that the proposed framework is able to build a wide range of fast image filtering that yields visually compelling results.     

Fast High‐Dimensional Filtering Using the Permutohedral Lattice

Many useful algorithms for processing images and geometry fall under the general framework of high‐dimensional Gaussian filtering. This family of algorithms includes bilateral filtering and non‐local means. We propose a new way to perform such filters using the permutohedral lattice, which tessellates high‐dimensional space with uniform simplices. Our algorithm is the first implementation of a high‐dimensional Gaussian filter that is both linear in input size and polynomial in dimensionality. Furthermore it is parameter‐free, apart from the filter size, and achieves a consistently high accuracy relative to ground truth (> 45 dB). We use this to demonstrate a number of interactive‐rate applications of filters in as high as eight dimensions.     

Registration Based Non‐uniform Motion Deblurring

This paper proposes an algorithm which uses image registration to estimate a non‐uniform motion blur point spread function (PSF) caused by camera shake. Our study is based on a motion blur model which models blur effects of camera shakes using a set of planar perspective projections (i.e., homographies). This representation can fully describe motions of camera shakes in 3D which cause non‐uniform motion blurs. We transform the non‐uniform PSF estimation problem into a set of image registration problems which estimate homographies of the motion blur model one‐by‐one through the Lucas‐Kanade algorithm. We demonstrate the performance of our algorithm using both synthetic and real world examples. We also discuss the effectiveness and limitations of our algorithm for non‐uniform deblurring.     

Image Appearance Exploration by Model-Based Navigation

Changing the appearance of an image can be a complex and non-intuitive task. Many times the target image colors and look are only known vaguely and many trials are needed to reach the desired results. Moreover, the effect of a specific change on an image is difficult to envision, since one must take into account spatial image considerations along with the color constraints. Tools provided today by image processing applications can become highly technical and non-intuitive including various gauges and knobs.
In this paper we introduce a method for changing image appearance by navigation, focusing on recoloring images. The user visually navigates a high dimensional space of possible color manipulations of an image. He can either explore in it for inspiration or refine his choices by navigating into sub regions of this space to a specific goal. This navigation is enabled by modeling the chroma channels of an image's colors using a Gaussian Mixture Model (GMM). The Gaussians model both color and spatial image coordinates, and provide a high dimensional parameterization space of a rich variety of color manipulations. The user's actions are translated into transformations of the parameters of the model, which recolor the image. This approach provides both inspiration and intuitive navigation in the complex space of image color manipulations.     

BSSRDF Estimation from Single Images

We present a novel method to estimate an approximation of the reflectance characteristics of optically thick, homogeneous translucent materials using only a single photograph as input. First, we approximate the diffusion profile as a linear combination of piecewise constant functions, an approach that enables a linear system minimization and maximizes robustness in the presence of suboptimal input data inferred from the image. We then fit to a smoother monotonically decreasing model, ensuring continuity on its first derivative. We show the feasibility of our approach and validate it in controlled environments, comparing well against physical measurements from previous works. Next, we explore the performance of our method in uncontrolled scenarios, where neither lighting nor geometry are known. We show that these can be roughly approximated from the corresponding image by making two simple assumptions: that the object is lit by a distant light source and that it is globally convex, allowing us to capture the visual appearance of the photographed material. Compared with previous works, our technique offers an attractive balance between visual accuracy and ease of use, allowing its use in a wide range of scenarios including off‐the‐shelf, single images, thus extending the current repertoire of real‐world data acquisition techniques.     

High-Quality Volumetric Reconstruction on Optimal Lattices for Computed Tomography

Within the context of emission tomography, we study volumetric reconstruction methods based on the Expectation Maximization (EM) algorithm. We show, for the first time, the equivalence of the standard implementation of the EM-based reconstruction with an implementation based on hardware-accelerated volume rendering for nearest-neighbor (NN) interpolation. This equivalence suggests that higher-order kernels should be used with caution and do not necessarily lead to better performance. We also show that the EM algorithm can easily be adapted for different lattices, the body-centered cubic (BCC) one in particular. For validation purposes, we use the 3D version of the Shepp-Logan synthetic phantom, for which we derive closed-form analytical expressions of the projection data. The experimental results show the theoretically-predicted optimality of NN interpolation in combination with the EM algorithm, for both the noiseless and the noisy case. Moreover, reconstruction on the BCC lattice leads to superior accuracy, more compact data representation, and better noise reduction compared to the Cartesian one. Finally, we show the usefulness of the proposed method for optical projection tomography of a mouse embryo.     

Shrinkability Maps for Content‐Aware Video Resizing

A novel method is given for content‐aware video resizing, i.e. targeting video to a new resolution (which may involve aspect ratio change) from the original. We precompute a per‐pixel cumulative shrinkability map which takes into account both the importance of each pixel and the need for continuity in the resized result. (If both x and y resizing are required, two separate shrinkability maps are used, otherwise one suffices). A random walk model is used for efficient offline computation of the shrinkability maps. The latter are stored with the video to create a multi‐sized video, which permits arbitrary‐sized new versions of the video to be later very efficiently created in real‐time, e.g. by a video‐on‐demand server supplying video streams to multiple devices with different resolutions. These shrinkability maps are highly compressible, so the resulting multi‐sized videos are typically less than three times the size of the original compressed video. A scaling function operates on the multi‐sized video, to give the new pixel locations in the result, giving a high‐quality content‐aware resized video. Despite the great efficiency and low storage requirements for our method, we produce results of comparable quality to state‐of‐the‐art methods for content‐aware image and video resizing.     

Fast ANN for High‐Quality Collaborative Filtering

Collaborative filtering collects similar patches, jointly filters them and scatters the output back to input patches; each pixel gets a contribution from each patch that overlaps with it, allowing signal reconstruction from highly corrupted data. Exploiting self‐similarity, however, requires finding matching image patches, which is an expensive operation. We propose a GPU‐friendly approximated‐nearest‐neighbour(ANN) algorithm that produces high‐quality results for any type of collaborative filter. We evaluate our ANN search against state‐of‐the‐art ANN algorithms in several application domains. Our method is orders of magnitudes faster, yet provides similar or higher quality results than the previous work.