Showing papers on "High-dynamic-range imaging published in 2001"

High Dynamic Range Imaging.

Abstract: Current display devices can display only a limited range of contrast and colors, which is one of the main reasons that most image acquisition, processing, and display techniques use no more than eight bits per color channel. This course outlines recent advances in high-dynamic-range imaging, from capture to display, that remove this restriction, thereby enabling images to represent the color gamut and dynamic range of the original scene rather than the limited subspace imposed by current monitor technology. This hands-on course teaches how high-dynamic-range images can be captured, the file formats available to store them, and the algorithms required to prepare them for display on low-dynamic-range display devices. The trade-offs at each stage, from capture to display, are assessed, allowing attendees to make informed choices about data-capture techniques, file formats, and tone-reproduction operators. The course also covers recent advances in image-based lighting, in which HDR images can be used to illuminate CG objects and realistically integrate them into real-world scenes. Through practical examples taken from photography and the film industry, it shows the vast improvements in image fidelity afforded by high-dynamic-range imaging.

Photocurrent estimation from multiple nondestructive samples in CMOS image sensor

Abstract: CMOS image sensors generally suffer form lower dynamic range than CCDs due to their higher readout noise. Their high speed readout capability and the potential of integrating memory and signal processing with the sensor on the same chip, open up many possibilities for enhancing their dynamic range. Earlier work have demonstrated the use of multiple non-destructive samples to enhance dynamic range, while achieving higher SNR than using other dynamic range enhancement schemes. The high dynamic range image is constructed by appropriately scaling each pixel's last sample before saturation. Conventional CDS is used to reduce offset FPN and reset noise. This simple high dynamic range image construction scheme, however, does not take full advantage of the multiple samples. Readout noise power, which doubles as a result of performing CDS, remain as high as in conventional sensor operation. As a result dynamic range is only extended at the high illumination end. The paper explores the use of linear mean-square-error estimation to more fully exploit the multiple pixel samples to reduce readout noise and thus extend dynamic range at the low illumination end. We present three estimation algorithms: (1) a recursive estimator when reset noise and offset FPN are ignored, (2) a non-recursive algorithm when reset noise and FPN are considered, and (3) a recursive estimation algorithm for case (2), which achieves mean square error close to the non-recursive algorithm without the need to store all the samples. The later recursive algorithm is attractive since it requires the storage of only a few pixel values per pixel, which makes its implementation in a single chip digital imaging system feasible.

Color Calibrated High Dynamic Range Imaging with ICC Profiles.

Abstract: High dynamic range (HDR) imaging has become a powerful tool in computer graphics, and is being applied to scenarios like simulation of different film responses, motion blur, and image-based illumination. The HDR images for these applications are typically generated by combining the information from multiple photographs taken at different exposure settings. Unfortunately, the color calibration of these images has so far been limited to very simplistic approaches such as a simple white balance algorithm. More sophisticated methods used for device-independent color representations are not easily applicable because they inherently assume a limited dynamic range. In this paper, we introduce a novel approach for constructing HDR images directly from low dynamic range images that were calibrated using an ICC input profile.

High Dynamic Range Imaging: Overview and Application

Abstract: The human eye is capable of perceiving contrast changes under a large variety of illumination conditions, thanks to its threshold adaptation property. This means the ability of the human visual system to perceive light intensities within wide dynamic ranges (the ratio of the highest and the lowest light value), even if every time a scene is observed the human brain performs some kind of selection on the perceived brightness values. In contrast, for example, the dynamic range of a typical Digital Still Camera (DSC) is limited by the imaging system sensor characteristics and A/D conversion allowing a maximum of about 8 bits (256 levels) of brightness information for each pixel. Thus when the dynamic range of the recorded image exceeds those of the image capturing medium, there will be, necessarily, a loss of details.