High-dynamic-range imaging

About: High-dynamic-range imaging is a research topic. Over the lifetime, 766 publications have been published within this topic receiving 22577 citations.

Accuracy of Luminance Maps Obtained from High Dynamic Range Images

Abstract: High dynamic range images can be converted into luminance maps if the response curve of the camera is known. Previous results for a consumer grade CCD camera have shown luminance errors as a function of Munsell hue and value. New measurements with a digital SLR camera with a CMOS sensor find two problems. First, saturated hues, especially blue, blue green, and purple are still difficult to measure. Second, surfaces with low reflectance below Munsell value N4 can be significantly overestimated, especially in spherical images obtained from the photography of mirror spheres. Nevertheless, it appears that HDR imaging is a suitable tool for luminance measurement if the scene contains luminance reference values such as calibrated matte color checkers and gray cards, and if dark surfaces and saturated hues are excluded. Spherical HDR images obtained from mirror spheres capture nearly the entire environment, but resolution and decreased sensitivity for surfaces with very low reflectance are still problems.

Context-dependent JPEG backward-compatible high-dynamic range image compression

Abstract: High-dynamic range (HDR) imaging is expected, together with ultrahigh definition and high-frame rate video, to become a technology that may change photo, TV, and film industries. Many cameras and displays capable of capturing and rendering both HDR images and video are already available in the market. The popularity and full-public adoption of HDR content is, however, hindered by the lack of standards in evalu- ation of quality, file formats, and compression, as well as large legacy base of low-dynamic range (LDR) displays that are unable to render HDR. To facilitate the wide spread of HDR usage, the backward compatibility of HDR with commonly used legacy technologies for storage, rendering, and compression of video and images are necessary. Although many tone-mapping algorithms are developed for generating viewable LDR con- tent from HDR, there is no consensus of which algorithm to use and under which conditions. We, via a series of subjective evaluations, demonstrate the dependency of the perceptual quality of the tone-mapped LDR images on the context: environmental factors, display parameters, and image con- tent itself. Based on the results of subjective tests, it proposes to extend JPEG file format, the most popular image format, in a backward compat- ible manner to deal with HDR images also. An architecture to achieve such backward compatibility with JPEG is proposed. A simple implementation of lossy compression demonstrates the efficiency of the proposed archi- tecture compared with the state-of-the-art HDR image compression. © 2013

Systems and methods for noise reduction in high dynamic range imaging

Abstract: This is generally directed to systems and methods for noise reduction in high dynamic range (“HDR”) imaging systems. In some embodiments, multiple images of the same scene can be captured, where each of the images is exposed for a different amount of time. An HDR image may be created by suitably combining the images. However, the signal-to-noise ratio (“SNR”) curve of the resulting HDR image can have discontinuities in sections of the SNR curve corresponding to shifts between different exposure times. Accordingly, in some embodiments, a noise model for the HDR image can be created that takes into account these discontinuities in the SNR curve. For example, a noise model can be created that smoothes the discontinuities of the SNR curve into a continuous function. This noise model may then be used with a Bayer Filter or any other suitable noise filter to remove noise from the HDR image.

High-dynamic range video acquisition with a multiview camera

Abstract: We propose a new methodology to acquire HDR video content for autostereoscopic displays by adapting and augmenting an eight view video camera with standard sensors. To augment the intensity capacity of the sensors, we combine images taken at dierent exposures. Since the exposure has to be the same for all objectives of our camera, we x the exposure variation by applying neutral density lters on each objective. Such an approach has two advantages: several exposures are known for each video frame and we do not need to worry about synchronization. For each pixel of each view, an HDR value is computed by a weighted average function applied to the values of matching pixels from all views. The building of the pixel match list is simplied by the property of our camera which has eight aligned, equally distributed objectives. At each frame, this results in an individual HDR image for each view while only one exposition per view was taken. The nal eight HDR images are tone-mapped and interleaved for autostereoscopic display.