Generalized Assorted Pixel Camera: Postcapture Control of Resolution, Dynamic Range, and Spectrum

By Fumihito Yasuma, Tomoo Mitsunaga, Daisuke Iso, Shree K. Nayar. IEEE Transactions on Image Processing, 2010

1,206 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/23b453f4129827eed425713c3c61fd877195de67

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In Atomic and Molecular Physics, and Optics

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Citations in the corpus

What is the space of spectral sensitivity functions for digital color cameras? (2013, background): “The multispectral images are from the database [25].”
Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery (2018, methodology): “The Columbia MSI Database [71] 11 is utilized in our simulated experiments.”
Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019, methodology): “The CAVE data-base [46] has 32 indoor HSIs captured by generalized assorted pixel camera.”
Hyperspectral Super-Resolution by Coupled Spectral Unmixing (2015, methodology): “As baselines to compare against, we use five state-of-the-art methods [1, 2, 21, 27, 29], in the authors' 2 Nikon D700, www.maxmax.com/spectral_response.htm CAVE database [28] Method RMSE SAM Aver.”
High-quality hyperspectral reconstruction using a spectral prior (2017, methodology): “We created an additional training dataset of 192 hyperspectral images, using 32 hyperspectral reflectance images from the Columbia [Yasuma et al. 2010] dataset under five different color temperatures of 2000◦K (CIE A, tungsten), 4000◦K (fluorescent light), 5000◦K (CIE D50), 6500◦K (CIE D65), and 13,”
Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization (2017, methodology): “In this section, experiments are conducted on two public databases (CAVE database [37](1) and Harvard database [8](2)) to evaluate the effectiveness of the proposed NLSTF method.”
Deep Hyperspectral Image Sharpening (2018, neutral): “The first row: images from the CAVE database [52].”
Bayesian sparse representation for hyperspectral image super resolution (2015, methodology): “on image ‘Spools’ (CAVE database) [30].”
Statistics of real-world hyperspectral images (2011, methodology): “General color filter array patterns (such as those proposed in [41]) can be designed to trade off spatial and spectral accuracy based on the relative variances of different components, and reconstruction methods can use the joint coefficient models during estimation.”
High-resolution hyperspectral imaging via matrix factorization (2011, methodology): “We have conducted experiments using a spectral image database described in [33], which provides spectral images of 32 scenes, sampled every 10 nm from 400 nm to 700 nm, by using a liquid crystal tunable filter, VariSpec [31] with a black/white cooled CCD camera, Apogee Alta U260 [3].”