A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and produces good results for a wide variety of images.

We present blue-c, a new immersive projection and 3D video acquisition environment for virtual design and collaboration. It combines simultaneous acquisition of multiple live video streams with advanced 3D projection technology in a CAVE^TM-like environment, creating the impression of total immersion. The blue-c portal currently consists of three rectangular projection screens that are built from glass panels containing liquid crystal layers. These screens can be switched from a whitish opaque state (for projection) to a transparent state (for acquisition), which allows the video cameras to "look through" the walls. Our projection technology is based on active stereo using two LCD projectors per screen. The projectors are synchronously shuttered along with the screens, the stereo glasses, active illumination devices, and the acquisition hardware. From multiple video streams, we compute a 3D video representation of the user in real time. The resulting video inlays are integrated into a networked virtual environment. Our design is highly scalable, enabling blue-c to connect to portals with less sophisticated hardware.

This paper draws from art history and perception to place computer depiction in the broader context of picture production. It highlights the often underestimated complexity of the interactions between features in the picture and features of the represented scene. Depiction is not always a unidirectional projection from a 3D scene to a 2D picture, but involves much feedback and influence from the picture space to the object space. Depiction can be seen as a pre-existing 3D reality projected onto 2D, but also as a 2D pictorial representation that is superficially compatible with an hypothetic 3D scene. We show that depiction is essentially an optimization problem, producing the best picture given goals and constraints. We introduce a classification of basic depiction techniques based on four kinds of issue. The spatial system deals with the mapping of spatial properties between 3D and 2D (including, but not restricted to, perspective projection). The primitive system deals with the dimensionality and mappings between picture primitives and scene primitives. Attributes deal with the assignment of visual properties such as colors, texture, or thickness. Finally, marks are the physical implementations of the picture (e.g. brush strokes, mosaic cells). A distinction is introduced between interaction and picturegeneration methods, and techniques are then organized depending on the dimensionality of the inputs and outputs.

We present a framework for automatically enhancing videos of a static scene using a few photographs of the same scene. For example, our system can transfer photographic qualities such as high resolution, high dynamic range and better lighting from the photographs to the video. Additionally, the user can quickly modify the video by editing only a few still images of the scene. Finally, our system allows a user to remove unwanted objects and camera shake from the video. These capabilities are enabled by two technical contributions presented in this paper. First, we make several improvements to a state-of-the-art multiview stereo algorithm in order to compute view-dependent depths using video, photographs, and structure-from-motion data. Second, we present a novel image-based rendering algorithm that can re-render the input video using the appearance of the photographs while preserving certain temporal dynamics such as specularities and dynamic scene lighting.

Color images have a gamut that typically spans three dimensions. Nevertheless, several important applications, such as the creation of grayscale images for printing and the re-coloring ofimagesforcolor-deficient viewers, require a reduction of gamut dimension. This paper describes a technique for preserving visual detail while reducing gamut dimension. The technique is derived by focusing on the problem of converting color imagestograyscale. A straightforward extension is then provided that allows re-coloring images for color-deficient viewers. Care is taken so that the resulting images remain within the available gamut and visual artifacts are not introduced.

Abstract. Under dimly lit condition, it is difficult to take a satisfactory image in long exposure time with a hand-held camera. Despite the use of a tripod, moving objects in the scene still generate ghosting and blurring effect. In this paper, we propose a novel approach to recover a high-quality image by exploiting the tradeoff between exposure time and motion blur, which considers color statistics and spatial constraints simultaneously, by using only two defective input images. A Bayesian framework is adopted to incorporate the factors to generate an optimal color mapping function. No estimation of PSF is performed. Our new approach can be readily extended to handle high contrast scenes to reveal fine details in saturated or highlight regions. An image acquisition system deploying off-the-shelf digital cameras and camera control softwares was built. We present our results on a variety of defective images: global and local motion blur due to camera shake or object movement, and saturation due to high contrast scenes. 1

In this paper we present a non-interactive method for recoloring a destination image according to the color scheme found in a source image. The approach is motivated by trying to invert the working process employed in oil painting, and results are demonstrated by application to several well-known oil paintings. The algorithm uses several color models, but leans most heavily on the L## color space. We first color segment each image bottomup by iteratively merging groups of pixels into connected regions of similar color. During color segmentation, a color "texture" tree is generated and associated to each region. Next, we construct classes of regions by compensating for color duplication and color similarity within the set of averaged color values obtained from regions. We extract a color palette for each image by choosing the colors of canonical region representatives from these classes. Once this palette is constructed for each image, any inverse map from the set of destination palette colors to the set of source palette colors induces a forward map from the classes of regions in the source image to sets of classes of regions in the destination image. For each source class in the range of the inverse map we transfer color from its canonical region representative to each of the associated destination regions. Color transfer occurs at the level of pixels, and uses the color texture trees associated to the regions. Our recoloring method attempts to maintain the destination image's original value structure. This is accomplished by transferring only the # and # channels from the source. To make our method computationally tractable, we work within an image pyramid, transferring color layer by layer.

This paper addresses the problem of shadow extraction from a single image of a complex natural scene. No simplifying assumption on the camera and the light source other than the Lambertian assumption is used. Our method is unique because it is capable of translating very rough usersupplied hints into the effective likelihood and prior functions for our Bayesian optimization. The likelihood function requires a decent estimation of the shadowless image, which is obtained by solving the associated Poisson equation. Our Bayesian framework allows for the optimal extraction of smooth shadows while preserving texture appearance under the extracted shadow. Thus our technique can be applied to shadow removal, producing some best results to date compared with the current state-of-the-art techniques using a single input image. We propose related applications in shadow compositing and image repair using our Bayesian technique. 1

This paper presents a novel approach to grayscale image matting and colorization. The first part of this approach is an efficient grayscale image matting algorithm in Bayesian framework. The foreground and background color distributions, and the alpha's distribution are modelled with spatially varying sets of Gaussians. The major novelties of this matting algorithm are the introduction of alpha's distribution and gradient into the Bayesian framework and an efficient optimization scheme. This grayscale image matting algorithm can effectively handle objects with intricate and vision sensitive boundaries, such as hair strands or facial organs. In the second part, by combining the grayscale image matting algorithm with color transferring techniques, an efficient colorization scheme is proposed, which provides great improvement over existing techniques for some difficult cases, such as human faces or images with confusing luminance distribution.

Abstract—This paper presents a voting method to perform image correction by global and local intensity alignment. The key to our modeless approach is the estimation of global and local replacement functions by reducing the complex estimation problem to the robust 2D tensor voting in the corresponding voting spaces. No complicated model for replacement function (curve) is assumed. Subject to the monotonic constraint only, we vote for an optimal replacement function by propagating the curve smoothness constraint using a dense tensor field. Our method effectively infers missing curve segments and rejects image outliers. Applications using our tensor voting approach are proposed and described. The first application consists of image mosaicking of static scenes, where the voted replacement functions are used in our iterative registration algorithm for computing the best warping matrix. In the presence of occlusion, our replacement function can be employed to construct a visually acceptable mosaic by detecting occlusion which has large and piecewise constant color. Furthermore, by the simultaneous consideration of color matches and spatial constraints in the voting space, we perform image intensity compensation and high contrast image correction using our voting framework, when only two defective input images are given. Index Terms—Image correction and recovery, color transfer, replacement functions, applications. 1