The bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges. It has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics, and fast versions have been proposed. Unfortunately, little is known about the accuracy of such accelerations. In this paper, we propose a new signal-processing analysis of the bilateral filter which complements the recent studies that analyzed it as a PDE or as a robust statistical estimator. The key to our analysis is to express the filter in a higher-dimensional space where the signal intensity is added to the original domain dimensions. Importantly, this signal-processing perspective allows us to develop a novel bilateral filtering acceleration using downsampling in space and intensity. This affords a principled expression of accuracy in terms of bandwidth and sampling. The bilateral filter can be expressed as linear convolutions in this augmented space followed by two simple nonlinearities. This allows us to derive criteria for downsampling the key operations and achieving important acceleration of the bilateral filter. We show that, for the same running time, our method is more accurate than previous acceleration techniques. Typically, we are able to process a 2 megapixel image using our acceleration technique in less than a second, and have the result be visually similar to the exact computation that takes several tens of minutes. The acceleration is most effective with large spatial kernels. Furthermore, this approach extends naturally to color images and cross bilateral filtering. 1

Figure 1: The Multi-Light Image Collection for this chard leaf contains 3 images taken under varying lighting conditions. The shading in each input image reveals different aspects of its shape and surface details. We combine the shading at multiple scales across the input images to generate the enhanced results. The result on the left exaggerates surface details by eliminating shadows, but yields a flat look. The result on the right is less extreme and includes some shadows to increase the perception of depth, at the cost of reducing some visible detail in the shadow regions. We present a new image-based technique for enhancing the shape and surface details of an object. The input to our system is a small set of photographs taken from a fixed viewpoint, but under varying lighting conditions. For each image we compute a multiscale decomposition based on the bilateral filter and then reconstruct an enhanced image that combines detail information at each scale across all the input images. Our approach does not require any information about light source positions, or camera calibration, and can produce good results with 3 to 5 input images. In addition our system provides a few high-level parameters for controlling the amount of enhancement and does not require pixel-level user input. We show that the bilateral filter is a good choice for our multiscale algorithm because it avoids the halo artifacts commonly associated with the traditional Laplacian image pyramid. We also develop a new scheme for computing our multiscale bilateral decomposition that is simple to implement, fast O(N 2 logN) and accurate.

Figure 1: We decompose a time-lapse sequence of photographs (a) into sun, sky, shadow, and reflectance components. The representation permits re-rendering without shadows (b) and without skylight (c), or modifying the reflectance of surfaces in the scene (d). We describe a method for converting time-lapse photography captured with outdoor cameras into Factored Time-Lapse Video (FTLV): a video in which time appears to move faster (i.e., lapsing) and where data at each pixel has been factored into shadow, illumination, and reflectance components. The factorization allows a user to easily relight the scene, recover a portion of the scene geometry (normals), and to perform advanced image editing operations. Our method is easy to implement, robust, and provides a compact representation with good reconstruction characteristics. We show results using several publicly available time-lapse sequences. CR Categories: I.4.8 [Image Processing and Computer Vision]: Scene Analysis—Time-varying Imagery I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture Keywords: Image-based rendering and lighting, inverse problems, computational photography, reflectance 1

Figure 1: The figure shows some of the image-enhancement filters we have created using the GradientShop optimization-framework. GradientShop has been designed to allow applications to explore gradient-domain solutions for various image processing problems. We present an optimization framework for exploring gradientdomain solutions for image and video processing. The proposed framework unifies many of the key ideas in the gradient-domain literature under a single optimization formulation. Our hope is that this generalized framework will allow the reader to quickly gain a general understanding of the field and contribute new ideas of their own. We propose a novel metric for measuring local gradient-saliency that identifies salient gradients that give rise to long, coherent edges, even when the individual gradients are faint. We present a general weighting-scheme for gradient-constraints that improves the visual appearance of results. We also provide a solution for applying gradient-domain filters to videos and video streams in a coherent manner. Finally, we demonstrate the utility of our formulation in creating effective yet simple to implement solutions for various imageprocessing tasks. To exercise our formulation we have created a new saliency-based sharpen filter and a pseudo image-relighting application. We also revisit and improve upon previously defined filters such as non-photorealistic rendering, image de-blocking, and sparse data interpolation over images (e.g., colorization using optimization). 1

Abstract. In this paper, we propose a novel type of explicit image filter- guided filter. Derived from a local linear model, the guided filter generates the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can perform as an edge-preserving smoothing operator like the popular bilateral filter [1], but has better behavior near the edges. It also has a theoretical connection with the matting Laplacian matrix [2], so is a more generic concept than a smoothing operator and can better utilize the structures in the guidance image. Moreover, the guidedfilterhasafastandnon-approximatelinear-time algorithm, whose computational complexity is independent of the filtering kernel size. We demonstrate that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling. 1

Abstract—We present a nonphotorealistic rendering technique that automatically delivers a stylized abstraction of a photograph. Our approach is based on shape/color filtering guided by a vector field that describes the flow of salient features in the image. This flow-based filtering significantly improves the abstraction performance in terms of feature enhancement and stylization. Our method is simple, fast, and easy to implement. Experimental results demonstrate the effectiveness of our method in producing stylistic and feature-enhancing illustrations from photographs. Index Terms—Nonphotorealistic rendering, image abstraction, flow-based filtering, line drawing, bilateral filter. Ç 1

Abstract. Video streams are ubiquitous in applications such as surveillance, games, and live broadcast. Processing and analyzing these data is challenging because algorithms have to be efficient in order to process the data on the fly. From a theoretical standpoint, video streams have their own specificities – they mix spatial and temporal dimensions, and compared to standard video sequences, half of the information is missing, i.e. the future is unknown. The theoretical part of our work is motivated by the ubiquitous use of the Gaussian kernel in tools such as bilateral filtering and mean-shift segmentation. We formally derive its equivalent for video streams as well as a dedicated expression of isotropic diffusion. Building upon this theoretical ground, we adapt a number of classical powerful algorithms to video streams: bilateral filtering, mean-shift segmentation, and anisotropic diffusion. 1

Figure 1: A puppeteer (left) manipulates cutout paper puppets tracked in real time (above) to control an animation (below). We present a video-based interface that allows users of all skill levels to quickly create cutout-style animations by performing the character motions. The puppeteer first creates a cast of physical puppets using paper, markers and scissors. He then physically moves these puppets to tell a story. Using an inexpensive overhead camera our system tracks the motions of the puppets and renders them on a new background while removing the puppeteer’s hands. Our system runs in real-time (at 30 fps) so that the puppeteer and the audience can immediately see the animation that is created. Our system also supports a variety of constraints and effects including articulated characters, multi-track animation, scene changes, camera controls, 21 /2-D environments, shadows, and animation cycles. Users have evaluated our system both quantitatively and qualitatively: In tests of low-level dexterity, our system has similar accuracy to a mouse interface. For simple story telling, users prefer our system over either a mouse interface or traditional puppetry. We demonstrate that even first-time users, including an eleven-yearold, can use our system to quickly turn an original story idea into an animation.

Recent research on the human visual system shows that our perception of object shape relies in part on compression and stretching of the reflected lighting environment onto its surface. We use this property to enhance the shape depiction of 3D objects by locally warping the environment lighting around main surface features. Contrary to previous work, which require specific illumination, material characteristics and/or stylization choices, our approach enhances surface shape without impairing the desired appearance. Thanks to our novel local shape descriptor, salient surface features are explicitly extracted in a view-dependent fashion at various scales without the need of any pre-process. We demonstrate our system on a variety of rendering settings, using object materials ranging from diffuse to glossy, to mirror or refractive, with direct or global illumination, and providing styles that range from photorealistic to non-photorealistic. The warping itself is very fast to compute on modern graphics hardware, enabling real-time performance in direct illumination scenarios.

We present an interactive abstract painting system named Sisley. Sisley works upon the psychological principle [Berlyne 1971] that abstract arts are often characterized by their greater perceptual ambiguities than photographs, which tend to invoke moderate mental efforts of the audience for interpretation, accompanied with subtle aesthetic pleasures. Given an input photograph, Sisley decomposes it into a hierarchy/tree of its constituent image components (e.g., regions, objects of different categories) with interactive guidance painting images, with increased ambiguities of both the scene and individual objects at desired levels. Sisley consists of three major working parts: (1) an interactive image parser executing the tasks of segmentation, labeling, and hierarchical organization, (2) a painterly rendering engine with abstract operators for transferring the image appearance, and (3) a numerical ambiguity computation and control module of servomechanism. With the help of Sisley, even an amateur user can create abstract paintings from photographs easily in minutes. We have evaluated the rendering results of Sisley using human experiments, and verified that they have similar abstract effects to original abstract paintings by artists.