We describe an interactive, computer-assisted framework for combining parts of a set of photographs into a single composite picture, a process we call "digital photomontage." Our framework makes use of two techniques primarily: graph-cut optimization, to choose good seams within the constituent images so that they can be combined as seamlessly as possible; and gradient-domain fusion, a process based on Poisson equations, to further reduce any remaining visible artifacts in the composite. Also central to the framework is a suite of interactive tools that allow the user to specify a variety of high-level image objectives, either globally across the image, or locally through a painting-style interface. Image objectives are applied independently at each pixel location and generally involve a function of the pixel values (such as "maximum contrast") drawn from that same location in the set of source images. Typically, a user applies a series of image objectives iteratively in order to create a finished composite. The power of this framework lies in its generality; we show how it can be used for a wide variety of applications, including "selective composites" (for instance, group photos in which everyone looks their best), relighting, extended depth of field, panoramic stitching, clean-plate production, stroboscopic visualization of movement, and time-lapse mosaics.

A new method is presented that takes as an input a high dynamic range image and maps it into a limited range of luminance values reproducible by a display device. There is significant evidence that a similar operation is performed by early stages of human visual system (HVS). Our approach follows functionality of HVS without attempting to construct its sophisticated model. The operation is performed in three steps. First, we estimate local adaptation luminance at each point in the image. Then, a simple function is applied to these values to compress them into the required display range. Since important image details can be lost during this process, we then re-introduce details in the final pass over the image.

Figure 1: (a) Top: Photograph taken in a dark environment, the image is noisy and/or blurry. Bottom: Flash photography provides a sharp but flat image with distracting shadows at the silhouette of objects. (b) Inset showing the noise of the available-light image. (c) Our technique merges the two images to transfer the ambiance of the available lighting. Note the shadow of the candle on the table. Our technique enhances photographs shot in dark environments by combining a picture taken with the available light and one taken with the flash. We preserve the ambiance of the original lighting and insert the sharpness. We use the bilateral filter to decompose the images into detail and large scale. We reconstruct the image using the large scale of the available lighting and the detail of the flash. We detect and correct flash shadow. Our output combines the advantages of available illumination and flash photography.

We propose a fast, high quality tone mapping technique to display high contrast images on devices with limited dynamic range of luminance values. The method is based on logarithmic compression of luminance values, imitating the human response to light. A bias power function is introduced to adaptively vary logarithmic bases, resulting in good preservation of details and contrast. To improve contrast in dark areas, changes to the gamma correction procedure are proposed. Our adaptive logarithmic mapping technique is capable of producing perceptually tuned images with high dynamic content and works at interactive speed. We demonstrate a successful application of our tone mapping technique with a high dynamic range video player enabling to adjust optimal viewing conditions for any kind of display while taking into account user preference concerning brightness, contrast compression, and detail reproduction.

We present a method for creating black-and-white illustrations from photographs of human faces. In addition an interactive technique is demonstrated for deforming these black-and-white facial illustrations to create caricatures which highlight and exaggerate representative facial features. We evaluate the effectiveness of the resulting images through psychophysical studies to assess accuracy and speed in both recognition and learning tasks. These studies show that the facial illustrations and caricatures generated using our techniques are as effective as photographs in recognition tasks. For the learning task we find that illustrations are learned two times faster than photographs and caricatures are learned one and a half times faster than photographs. Because our techniques produce images that are effective at communicating complex information, they are useful in a number of potential applications, ranging from entertainment and education to low bandwidth telecommunications and psychology research. Categories and Subject Descriptors: I.3.3 [Computer Graphics]: Picture/image Generation—bitmap and framebuffer operations;

This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization, and the creation of panoramic mosaics. Image stitching algorithms take the alignment estimates produced by such registration algorithms and blend the images in a seamless manner, taking care to deal with potential problems such as blurring or ghosting caused by parallax and scene movement as well as varying image exposures. This tutorial reviews the basic motion models underlying alignment and stitching algorithms, describes effective direct (pixel-based) and feature-based alignment algorithms, and describes blending algorithms used to produce seamless mosaics. It ends with a discussion of open research problems in the area. 1

Due to rapid technological progress in high dynamic range (HDR) video capture and display, the efficient storage and transmission of such data is crucial for the completeness of any HDR imaging pipeline. We propose a new approach for inter-frame encoding of HDR video, which is embedded in the well-established MPEG4 video compression standard. The key component of our technique is luminance quantization that is optimized for the contrast threshold perception in the human visual system. The quantization scheme requires only 10--11 bits to encode 12 orders of magnitude of visible luminance range and does not lead to perceivable contouring artifacts. Besides video encoding, the proposed quantization provides perceptually-optimized luminance sampling for fast implementation of any global tone mapping operator using a lookup table. To improve the quality of synthetic video sequences, we introduce a coding scheme for discrete cosine transform (DCT) blocks with high contrast. We demonstrate the capabilities of HDR video in a player, which enables decoding, tone mapping, and applying post-processing effects in real-time. The tone mapping algorithm as well as its parameters can be changed interactively while the video is playing. We can simulate post-processing effects such as glare, night vision, and motion blur, which appear very realistic due to the usage of HDR data.

High dynamic range (HDR) imaging is an area of increasing importance, but most display devices still have limited dynamic range (LDR). Various techniques have been proposed for compressing the dynamic range while retaining important visual information. Multiscale image processing techniques, which are widely used for many image processing tasks, have a reputation of causing halo artifacts when used for range compression. However, we demonstrate that they can work when properly implemented. We use a symmetrical analysis-synthesis filter bank, and apply local gain control to the subbands. We also show that the technique can be adapted for the related problem of “companding”, in which an HDR image is converted to an LDR image, and later expanded back to high dynamic range.

Tone mapping operators are designed to reproduce visibility and the overall impression of brightness, contrast and color of the real world onto limited dynamic range displays and printers. Although many tone mapping operators have been published in recent years, no thorough psychophysical experiments have yet been undertaken to compare such operators against the real scenes they are purporting to depict. In this paper, we present the results of a series of psychophysical experiments to validate six frequently used tone mapping operators against linearly mapped High Dynamic Range (HDR) scenes displayed on a novel HDR device. Individual operators address the tone mapping issue using a variety of approaches and the goals of these techniques are often quite different from one another. Therefore, the purpose of this investigation was not simply to determine which is the "best" algorithm, but more generally to propose an experimental methodology to validate such operators and to determine the participants' impressions of the images produced compared to what is visible on a high contrast ratio display.

The ultimate aim of realistic graphics is the creation of images that provoke the same responses that a viewer would have to a real scene. This STAR addresses two related key problem areas in this effort which are located at opposite ends of the rendering pipeline, namely the data structures used to describe light during the actual rendering process, and the issue of displaying such radiant intensities in a meaningful way.