High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting

By E. Reinhard, G. Ward, S. Pattanaik, Paul E. Debevec, Wolfgang Heidrich, Karol Myszkowski. 2010

1,545 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/79d0b5216b27bdc972a98162fd9308c61dacb4db

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Citations in the corpus

Objective Quality Assessment of Tone-Mapped Images (2013, background): “T HERE has been a growing interest in recent years in high dynamic range (HDR) images, where the range of intensity levels could be on the order of 10,000 to 1 [1].”
Learning photographic global tonal adjustment with a database of input/output image pairs (2011, background): “Tone mapping algorithms [15] compress the tonal range of HDR images.”
Learning to predict indoor illumination from a single image (2017, background): “Follow-up work has demonstrated that the same can be done from a single shot, provided there is also a diffuse sphere in the scene [Reinhard et al. 2010], or a metallic/diffuse hybrid sphere [Debevec et al. 2012].”
Developing serious games for cultural heritage: a state-of-the-art review (2010, background): “It stems from the fact that real world luminance has a very high dynamic range, which means that bright surface patches are several orders of magnitude brighter than dark surface patches—for example, the sun at noon ‘‘may be 100 million times brighter than starlight’’ (Reinhard et al. 2006).”
End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging (2018, background): “2010; Dowski and Cathey 1995], dynamic range [Debevec and Malik 1997; Mann et al. 1995; Reinhard et al. 2005; Rouf et al. 2011], and image resolution [Ben-Ezra et al.”
Perceptual issues in augmented reality revisited (2010, background): “Color and contrast, and potential depth problems can be improved by using a better lens and a higher resolution sensor, or by using high dynamic range (HDR) imaging (Reinhard et al. [47]).”
Nonlinear disparity mapping for stereoscopic 3D (2010, methodology): “Inspired by work on content retargeting and tone mapping [Reinhard et al. 2005; Weyrich et al. 2007; Shamir and Sorkine 2009] we present a solution for general nonlinear disparity mapping operators for stereoscopic 3D in Section 3.”
Burst photography for high dynamic range and low-light imaging on mobile cameras (2016, methodology): “This process is called HDR tone mapping, and has been well studied [Reinhard et al. 2010].”
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images (2018, background): “Because of the limited dynamic range, traditional digital imaging systems may lose structural details when shooting a natural scene [30].”
Perceptual Quality Assessment for Multi-Exposure Image Fusion (2015, background): “MULTI-EXPOSURE image fusion (MEF) is considered an effective quality enhancement technique that is widely adopted in consumer electronics [1].”
Colorimetry (2020, unknown): “Tone mapping is the focus of extensive research [Reinhard, 2010; Mantiuk et al., 2015].”
Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach (2017, background): “Here xk for all k are column vectors of C N2 dimensions, where C is the number of color channels of the input images and N is the spatial size of a square patch.”
Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning (2011, background): “High dynamic range (HDR) images might also be acquired to recover all scene details [120] while color discontinuities and aliasing effects must be removed [121].”
HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition (2017, background): “The ability to accurately measure luminance is also limited by the low dynamic range of conventional cameras [5].”
Attention-Guided Network for Ghost-Free High Dynamic Range Imaging (2019, background): “The most common strategy is to take a series of LDR images at different exposures and then merge them into an HDR image [2, 21, 23, 29, 39].”