Showing papers on "High-dynamic-range imaging published in 2003"

High dynamic range video

Abstract: Typical video footage captured using an off-the-shelf camcorder suffers from limited dynamic range. This paper describes our approach to generate high dynamic range (HDR) video from an image sequence of a dynamic scene captured while rapidly varying the exposure of each frame. Our approach consists of three parts: automatic exposure control during capture, HDR stitching across neighboring frames, and tonemapping for viewing. HDR stitching requires accurately registering neighboring frames and choosing appropriate pixels for computing the radiance map. We show examples for a variety of dynamic scenes. We also show how we can compensate for scene and camera movement when creating an HDR still from a series of bracketed still photographs.

Estimation-theoretic approach to dynamic range enhancement using multiple exposures

Abstract: We present a new approach for improving the effective dynamic range of cameras by using multiple photographs of the same scene taken with different exposure times. Using this method enables the photographer to accurately capture scenes that contain high dynamic range by using a device with low dynamic range, which allows the capture of scenes that have both very bright and very dark regions. We approach the problem from a probabilistic standpoint, distinguishing it from the other methods reported in the literature on photographic dynamic range improvement. A new method is proposed for determining the camera's response function, which is an iterative procedure that need be done only once for a particular camera. With the response function known, high dynamic range images can be easily constructed by a weighted average of the input images. The particular form of weighting is controlled by the probabilistic formulation of the problem, and results in higher weight being assigned to pixels taken at longer exposure times. The advantages of this new weighting scheme are explained by com- parison with other methods in the literature. Experimental results are presented to demonstrate the utility of the algorithm. © 2003 SPIE

High dynamic range imaging for digital still camera: an overview

Abstract: We present a collection of methods and algorithms able to deal with high dynamic ranges of real pictures acquired by digital engines e.g., charge-coupled device (CCD/CMOS) cameras. An ac- curate image acquisition can be challenging under difficult light con- ditions. A few techniques that overcome dynamic range limitations problems are reported. The presented methods allow the recovery of the original radiance values of the final 8-bit-depth image starting from differently exposed pictures. This allows the capture of both low- and high-light details by merging the various pictures into a single map, thus providing a more faithful description of what the real world scene was. However, in order to be viewed on a common computer monitor, the map needs to be compressed and requan- tized while preserving the visibility of details. The main problem comes from the fact that the contrast of the radiance values is usu- ally far greater than that of the display device. Various related tech- niques are reviewed and discussed. © 2003 SPIE and IS&T.

Rendering HDR images

Abstract: Color imaging systems are continuously improving, and have now improved to the point of capturing high dynamic range scenes. Unfortunately most commercially available color display devices, such as CRTs and LCDs, are limited in their dynamic range. It is necessary to tone-map, or render, the high dynamic range images in order to display them onto a lower dynamic range device. This paper describes the use of an image appearance model, iCAM, to render high dynamic range images for display. Image appearance models have greater flexibility over dedicated tone-scaling algorithms as they are designed to predict how images perceptually appear, and not designed for the singular purpose of rendering. In this paper we discuss the use of an image appearance framework, and describe specific implementation details for using that framework to render high dynamic range images.

Imaging method and system

Abstract: A method and system for accurately imaging scenes having large brightness variations. If a particular object in the scene is of interest, the imager exposure setting is adjusted based on the brightness of that object. For high dynamic range imaging of an entire scene, two imagers with different viewpoints and exposure settings are used, or the exposure setting of a single imager is varied as multiple images are captured. An optical flow technique can be used to track and image moving objects, or a video sequence can be generated by selectively updating only those pixels whose brightnesses are within the preferred brightness range of the imager.

High dynamic range imaging: Spatially varying pixel exposures

Abstract: While real scenes produce a wide range of brightness variations, vision systems use low dynamic range image detectors that typically provide 8 bits of brightness data at each pixel. The resulting low quality images greatly limit what vision can accomplish today. This paper proposes a very simple method for significantly enhancing the dynamic range of virtually any imaging system. The basic principle is to simultaneously sample the spatial and exposure dimensions of image irradiance. One of several ways to achieve this is by placing an optical mask adjacent to a conventional image detector array. The mask has a pattern with spatially varying transmittance, thereby giving adjacent pixels on the detector different exposures to the scene. The captured image is mapped to a high dynamic range image using an efficient image reconstruction algorithm. The end result is an imaging system that can measure a very wide range of scene radiance and produce a substantially larger number of brightness levels, with a slight reduction in spatial resolution. We conclude with several examples of high dynamic range images computed using spatially varying pixel exposures.

Update on Reciprocity Effects for Accelerated Ozone Fade Testing of Inkjet Photographic Prints

Abstract: High Dynamic Range Imaging: Algorithms that Mimic Human Vision There have been many recent advances in rendering images in the low-dynamic-range prints and displays, including commercial products. The problem is analogous to human vision having receptors responsive to a range of 10 in radiance, while the optic nerve has a 2003-2004 IS&T Visiting Lecturer John McCann range of only 10. These highdynamic-range algorithms share a common mechanism, namely they are based on spatial comparison of pixels and regions in the captured image. These algorithms mimic human image processing in that they use multiresolution comparison techniques. As shown by Adams, and Jones & Condit, outdoor scenes typically have ranges in radiances between 10 to 10. Scenes with specular reflections, that are images of the sun, have much greater ranges. Tone scale transforms, such as S shaped H&D curves, cannot render output images to match human sensations. Tone scale transforms compress the highlights and shadows too much. Spatial comparison algorithms automatically “dodge and burn” the image based on the spatial content of the input image. They auto-matically generate the equivalent of scenedependent spatial frequency filters. Many other interesting visual phenomena can be modeled by processes based on spatial comparisons. Color constancy, visibility of gradients and edges, appearance of transparency, and color gamut transformations are more closely related than one might think. Experiments have shown that they share a common property, namely they can be explained by human spatial comparison mechanisms. A RO U N D TH E CO CIETY

Faithful presentation of luminance contrast: evaluation of photographic and computational display methods

Abstract: Representations necessarily lose some of the visual information available in corresponding real-world scenes. This paper will discuss evaluations of the extent to which luminance contrast and visibility is preserved with three different methods for representing real-world scenes. Method one involves using psychophysical data from contrast charts to select the best print from among a density-varied series of photographic prints. The second and third methods involve extending the dynamic range of the representation by using High Dynamic Range Image (HDRI) techniques. HDRI's can be created by combining multiple overlapping exposures of a scene, or via computer simulation. In method two, algorithms are used to compress the luminance information in the HDRI into the luminance range available in the display, while preserving visible contrast as much as possible. The third method uses a wide-field, high-dynamic-range viewer to present an image with a much wider dynamic range than is available in a photographic print or a CRT display. Each method represents an improvement over simple photographic representation. In conjunction with appropriate instructions on how to interpret the images and the extent to which the images can be regarded as faithful, methods such as these can support practical decisions in visual design and reconstruction.