Abstract

Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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2019 (2)

A. Sánchez de Miguel, C. C. Kyba, M. Aubé, J. Zamorano, N. Cardiel, C. Tapia, J. Bennie, and K. J. Gaston, “Colour remote sensing of the impact of artificial light at night (I): The potential of the International Space Station and other DSLR-based platforms,” Remote. Sens. Environ. 224, 92–103 (2019).
[Crossref]

M. Grossi, “A sensor-centric survey on the development of smartphone measurement and sensing systems,” Measurement 135, 572–592 (2019).
[Crossref]

2018 (19)

A. A. Zaidan, B. B. Zaidan, O. S. Albahri, M. A. Alsalem, A. S. Albahri, Q. M. Yas, and M. Hashim, “A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution,” Heal. Technol. 8, 223–238 (2018).
[Crossref]

S. Kanchi, M. I. Sabela, P. S. Mdluli, Inamuddin, and K. Bisetty, “Smartphone based bioanalytical and diagnosis applications: A review,” Biosens. Bioelectron. 102, 136–149 (2018).
[Crossref]

X. Huang, D. Xu, J. Chen, J. Liu, Y. Li, J. Song, X. Ma, and J. Guo, “Smartphone-based analytical biosensors,” Analyst 143, 5339–5351 (2018).
[Crossref] [PubMed]

A. J. S. McGonigle, T. C. Wilkes, T. D. Pering, J. R. Willmott, J. M. Cook, F. M. Mims, and A. V. Parisi, “Smartphone spectrometers,” Sensors 18, 1–15 (2018).
[Crossref]

P. C. Gray, J. T. Ridge, S. K. Poulin, A. C. Seymour, A. M. Schwantes, J. J. Swenson, and D. W. Johnston, “Integrating drone imagery into high resolution satellite remote sensing assessments of estuarine environments,” Remote. Sens. 10, 1257 (2018).
[Crossref]

T. Leeuw and E. Boss, “The HydroColor app: Above water measurements of remote sensing reflectance and turbidity using a smartphone camera,” Sensors 18, 256 (2018).
[Crossref]

Y. Yang, L. L. Cowen, and M. Costa, “Is ocean reflectance acquired by citizen scientists robust for science applications?” Remote. Sens. 10, 835 (2018).
[Crossref]

J. B. Gallagher and C. H. Chuan, “Chlorophyll a and turbidity distributions: Applicability of using a smartphone app across two contrasting bays,” J. Coast. Res. 34, 1236–1243 (2018).
[Crossref]

D. P. Igoe, A. V. Parisi, A. Amar, N. J. Downs, and J. Turner, “Atmospheric total ozone column evaluation with a smartphone image sensor,” Int. J. Remote. Sens. 39, 2766–2783 (2018).
[Crossref]

A. Hänel, T. Posch, S. J. Ribas, M. Aubé, D. Duriscoe, A. Jechow, Z. Kollath, D. E. Lolkema, C. Moore, N. Schmidt, H. Spoelstra, G. Wuchterl, and C. C. Kyba, “Measuring night sky brightness: methods and challenges,” J. Quant. Spectrosc. Radiat. Transf. 205, 278–290 (2018).
[Crossref]

M. Ruwaimana, B. Satyanarayana, V. Otero, A. M. Muslim, M. Syafiq A., S. Ibrahim, D. Raymaekers, N. Koedam, and F. Dahdouh-Guebas, “The advantages of using drones over space-borne imagery in the mapping of mangrove forests,” PLOS ONE 13, e0200288 (2018).
[Crossref] [PubMed]

R. G. Mannino, D. R. Myers, E. A. Tyburski, C. Caruso, J. Boudreaux, T. Leong, G. D. Clifford, and W. A. Lam, “Smartphone app for non-invasive detection of anemia using only patient-sourced photos,” Nat. Commun. 9, 4924 (2018).
[Crossref] [PubMed]

H. Ding, C. Chen, S. Qi, C. Han, and C. Yue, “Smartphone-based spectrometer with high spectral accuracy for mHealth application,” Sensors Actuators A: Phys. 274, 94–100 (2018).
[Crossref]

C. A. Coburn, A. M. Smith, G. S. Logie, and P. Kennedy, “Radiometric and spectral comparison of inexpensive camera systems used for remote sensing,” Int. J. Remote. Sens. 39, 4869–4890 (2018).
[Crossref]

Y. Q. Xu, J. Hua, Z. Gong, W. Zhao, Z. Q. Zhang, C. Y. Xie, Z. T. Chen, and J. F. Chen, “Visible light communication using dual camera on one smartphone,” Opt. Express 26, 34609–34621 (2018).
[Crossref]

R. A. Crocombe, “Portable Spectroscopy,” Appl. Spectrosc. 72, 1701–1751 (2018).
[Crossref]

S. Manfreda, M. McCabe, P. Miller, R. Lucas, V. Pajuelo Madrigal, G. Mallinis, E. Ben Dor, D. Helman, L. Estes, G. Ciraolo, J. Müllerová, F. Tauro, M. de Lima, J. de Lima, A. Maltese, F. Frances, K. Caylor, M. Kohv, M. Perks, G. Ruiz-Pérez, Z. Su, G. Vico, and B. Toth, “On the use of unmanned aerial systems for environmental monitoring,” Remote. Sens. 10, 641 (2018).
[Crossref]

C. E. Tapia Ayuga and J. Zamorano, “LICA AstroCalc, a software to analyze the impact of artificial light: Extracting parameters from the spectra of street and indoor lamps,” J. Quant. Spectrosc. Radiat. Transf. 214, 33–38 (2018).
[Crossref]

S. Chaji, A. Pourreza, H. Pourreza, and M. Rouhani, “Estimation of the camera spectral sensitivity function using neural learning and architecture,” J. Opt. Soc. Am. A 35, 850–858 (2018).
[Crossref]

2017 (9)

J. Turner, A. V. Parisi, D. P. Igoe, and A. Amar, “Detection of ultraviolet B radiation with internal smartphone sensors,” Instrumentation Sci. & Technol. 45, 618–638 (2017).
[Crossref]

M. Pagnutti, R. E. Ryan, G. Cazenavette, M. Gold, R. Harlan, E. Leggett, and J. Pagnutti, “Laying the foundation to use Raspberry Pi 3 V2 camera module imagery for scientific and engineering purposes,” J. Electron. Imaging 26, 013014 (2017).
[Crossref]

Y. Wang, M. M. A. Zeinhom, M. Yang, R. Sun, S. Wang, J. N. Smith, C. Timchalk, L. Li, Y. Lin, and D. Du, “A 3D-printed, portable, optical-sensing platform for smartphones capable of detecting the herbicide 2,4-dichlorophenoxyacetic acid,” Anal. Chem. 89, 9339–9346 (2017).
[Crossref] [PubMed]

L.-J. Wang, Y.-C. Chang, R. Sun, and L. Li, “A multichannel smartphone optical biosensor for high-throughput point-of-care diagnostics,” Biosens. Bioelectron. 87, 686–692 (2017).
[Crossref]

K. D. Long, E. V. Woodburn, H. M. Le, U. K. Shah, S. S. Lumetta, and B. T. Cunningham, “Multimode smartphone biosensing: the transmission, reflection, and intensity spectral (TRI)-analyzer,” Lab on a Chip 17, 3246–3257 (2017).
[Crossref] [PubMed]

F. Cai, W. Lu, W. Shi, and S. He, “A mobile device-based imaging spectrometer for environmental monitoring by attaching a lightweight small module to a commercial digital camera,” Sci. Reports 7, 15602 (2017).
[Crossref]

A. Friedrichs, J. A. Busch, H. J. van der Woerd, and O. Zielinski, “SmartFluo: A method and affordable adapter to measure chlorophyll a fluorescence with smartphones,” Sensors 17, 678 (2017).
[Crossref]

H. Kim, Y. Jung, I.-J. Doh, R. A. Lozano-Mahecha, B. Applegate, and E. Bae, “Smartphone-based low light detection for bioluminescence application,” Sci. Reports 7, 40203 (2017).
[Crossref]

G. Rateni, P. Dario, and F. Cavallo, “Smartphone-based food diagnostic technologies: A review,” Sensors 17, 1453 (2017).
[Crossref]

2016 (10)

F. Li, Y. Bao, D. Wang, W. Wang, and L. Niu, “Smartphones for sensing,” Sci. Bull. 61, 190–201 (2016).
[Crossref]

K. E. McCracken and J.-Y. Yoon, “Recent approaches for optical smartphone sensing in resource-limited settings: a brief review,” Anal. Methods 8, 6591–6601 (2016).
[Crossref]

D. Whiteson, M. Mulhearn, C. Shimmin, K. Cranmer, K. Brodie, and D. Burns, “Searching for ultra-high energy cosmic rays with smartphones,” Astropart. Phys. 79, 1–9 (2016).
[Crossref]

S. Levin, S. Krishnan, S. Rajkumar, N. Halery, and P. Balkunde, “Monitoring of fluoride in water samples using a smartphone,” Sci. The Total. Environ. 551–552, 101–107 (2016).
[Crossref]

C. Zhang, G. Cheng, P. Edwards, M.-D. Zhou, S. Zheng, and Z. Liu, “G-Fresnel smartphone spectrometer,” Lab on a Chip 16, 246–250 (2016).
[Crossref]

J. Rasmussen, G. Ntakos, J. Nielsen, J. Svensgaard, R. N. Poulsen, and S. Christensen, “Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?” Eur. J. Agron. 74, 75–92 (2016).
[Crossref]

F. Tauro, M. Porfiri, and S. Grimaldi, “Surface flow measurements from drones,” J. Hydrol. 540, 240–245 (2016).
[Crossref]

C.-H. Lin, K.-L. Chung, and C.-W. Yu, “Novel chroma subsampling strategy based on mathematical optimization for compressing mosaic videos with arbitrary RGB color filter arrays in H.264/AVC and HEVC,” IEEE Transactions on Circuits Syst. for Video Technol. 26, 1722–1733 (2016).
[Crossref]

V. D. Silva, V. Chesnokov, and D. Larkin, “A novel adaptive shading correction algorithm for camera systems,” Electron. Imaging 2016, 1–5 (2016).
[Crossref]

G. Finlayson, M. M. Darrodi, and M. Mackiewicz, “Rank-based camera spectral sensitivity estimation,” J. Opt. Soc. Am. A 33, 589–599 (2016).
[Crossref]

2015 (4)

P.-K. Yang, “Determining the spectral responsivity from relative measurements by using multicolor light-emitting diodes as probing light sources,” Optik 126, 3088–3092 (2015).
[Crossref]

D. Javoršek, T. Jerman, and A. Javoršek, “Comparison of two digital cameras based on spectral data estimation obtained with two methods,” Acta Polytech. Hungarica 12, 183–197 (2015).

M. M. Darrodi, G. Finlayson, T. Goodman, and M. Mackiewicz, “Reference data set for camera spectral sensitivity estimation,” J. Opt. Soc. Am. A 32, 381–391 (2015).
[Crossref]

S. Novoa, M. R. Wernand, and H. J. van der Woerd, “WACODI: A generic algorithm to derive the intrinsic color of natural waters from digital images,” Limnol. Oceanogr. Methods 13, 697–711 (2015).
[Crossref]

2014 (6)

F. Snik, J. H. H. Rietjens, A. Apituley, H. Volten, B. Mijling, A. Di Noia, S. Heikamp, R. C. Heinsbroek, O. P. Hasekamp, J. M. Smit, J. Vonk, D. M. Stam, G. van Harten, J. de Boer, C. U. Keller, and ISPEX citizen scientists, “Mapping atmospheric aerosols with a citizen science network of smartphone spectropolarimeters,” Geophys. Res. Lett. 41, 7351–7358 (2014).
[Crossref]

S. Sumriddetchkajorn, K. Chaitavon, and Y. Intaravanne, “Mobile-platform based colorimeter for monitoring chlorine concentration in water,” Sensors Actuators B: Chem. 191, 561–566 (2014).
[Crossref]

K. Flynn and S. Chapra, “Remote sensing of submerged aquatic vegetation in a shallow non-turbid river using an unmanned aerial vehicle,” Remote. Sens. 6, 12815–12836 (2014).
[Crossref]

X. Liang, A. Jaakkola, Y. Wang, J. Hyyppä, E. Honkavaara, J. Liu, and H. Kaartinen, “The use of a hand-held camera for individual tree 3D mapping in forest sample plots,” Remote. Sens. 6, 6587–6603 (2014).
[Crossref]

A. Skandarajah, C. D. Reber, N. A. Switz, D. A. Fletcher, and A. J. Kabla, “Quantitative imaging with a mobile phone microscope,” PLOS ONE 9, e96906 (2014).
[Crossref] [PubMed]

D. P. Igoe, A. V. Parisi, and B. Carter, “A method for determining the dark response for scientific imaging with smartphones,” Instrumentation Sci. Technol. 42, 586–592 (2014).
[Crossref]

2013 (1)

2011 (2)

Z. J. Smith, K. Chu, A. R. Espenson, M. Rahimzadeh, A. Gryshuk, M. Molinaro, D. M. Dwyre, S. Lane, D. Matthews, and S. Wachsmann-Hogiu, “Cell-phone-based platform for biomedical device development and education applications,” PLOS ONE 6, e17150 (2011).
[Crossref] [PubMed]

H. Zhang and E. Sanchez-Sinencio, “Linearization techniques for CMOS low noise amplifiers: A tutorial,” IEEE Transactions on Circuits Syst. I: Regul. Pap. 58, 22–36 (2011).
[Crossref]

2010 (2)

D. B. Goldman, “Vignette and exposure calibration and compensation,” IEEE Transactions on Pattern Analysis Mach. Intell. 32, 2276–2288 (2010).
[Crossref]

A. Kreuter, C. Emde, and M. Blumthaler, “Measuring the influence of aerosols and albedo on sky polarization,” Atmospheric Res. 98, 363–367 (2010).
[Crossref]

2009 (4)

L. Goddijn-Murphy, D. Dailloux, M. White, and D. G. Bowers, “Fundamentals of in situ digital camera methodology for water quality monitoring of coast and ocean,” Sensors 9, 5825–5843 (2009).
[Crossref] [PubMed]

A. Kreuter, M. Zangerl, M. Schwarzmann, and M. Blumthaler, “All-sky imaging: a simple, versatile system for atmospheric research,” Appl. Opt. 48, 1091–1097 (2009).
[Crossref] [PubMed]

Y. Zheng, S. Lin, C. Kambhamettu, J. Yu, and S. Bing Kang, “Single-image vignetting correction,” IEEE Transactions on Pattern Analysis Mach. Intell. 31, 2243–2256 (2009).
[Crossref]

F. Sigernes, M. Dyrland, N. Peters, D. A. Lorentzen, T. Svenøe, K. Heia, S. Chernouss, C. S. Deehr, and M. Kosch, “The absolute sensitivity of digital colour cameras,” Opt. Express 17, 20211–20220 (2009).
[Crossref] [PubMed]

2008 (2)

S. J. Kim and M. Pollefeys, “Robust radiometric calibration and vignetting correction,” IEEE Transactions on Pattern Analysis Mach. Intell. 30, 562–576 (2008).
[Crossref]

V. Lebourgeois, A. Bégué, S. Labbé, B. Mallavan, L. Prévot, and B. Roux, “Can commercial digital cameras be used as multispectral sensors? A crop monitoring test,” Sensors 8, 7300–7322 (2008).
[Crossref] [PubMed]

2007 (2)

D. Menon, S. Andriani, and G. Calvagno, “Demosaicing with directional filtering and a posteriori decision,” IEEE Transactions on Image Process. 16, 132–141 (2007).
[Crossref]

A. Cescatti, “Indirect estimates of canopy gap fraction based on the linear conversion of hemispherical photographs: Methodology and comparison with standard thresholding techniques,” Agric. For. Meteorol. 143, 1–12 (2007).
[Crossref]

2005 (1)

2001 (1)

C. A. Gueymard, “Parameterized transmittance model for direct beam and circumsolar spectral irradiance,” Sol. Energy 71, 325–346 (2001).
[Crossref]

Albahri, A. S.

A. A. Zaidan, B. B. Zaidan, O. S. Albahri, M. A. Alsalem, A. S. Albahri, Q. M. Yas, and M. Hashim, “A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution,” Heal. Technol. 8, 223–238 (2018).
[Crossref]

Albahri, O. S.

A. A. Zaidan, B. B. Zaidan, O. S. Albahri, M. A. Alsalem, A. S. Albahri, Q. M. Yas, and M. Hashim, “A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution,” Heal. Technol. 8, 223–238 (2018).
[Crossref]

Alsalem, M. A.

A. A. Zaidan, B. B. Zaidan, O. S. Albahri, M. A. Alsalem, A. S. Albahri, Q. M. Yas, and M. Hashim, “A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution,” Heal. Technol. 8, 223–238 (2018).
[Crossref]

Amar, A.

D. P. Igoe, A. V. Parisi, A. Amar, N. J. Downs, and J. Turner, “Atmospheric total ozone column evaluation with a smartphone image sensor,” Int. J. Remote. Sens. 39, 2766–2783 (2018).
[Crossref]

J. Turner, A. V. Parisi, D. P. Igoe, and A. Amar, “Detection of ultraviolet B radiation with internal smartphone sensors,” Instrumentation Sci. & Technol. 45, 618–638 (2017).
[Crossref]

Andriani, S.

D. Menon, S. Andriani, and G. Calvagno, “Demosaicing with directional filtering and a posteriori decision,” IEEE Transactions on Image Process. 16, 132–141 (2007).
[Crossref]

Apituley, A.

F. Snik, J. H. H. Rietjens, A. Apituley, H. Volten, B. Mijling, A. Di Noia, S. Heikamp, R. C. Heinsbroek, O. P. Hasekamp, J. M. Smit, J. Vonk, D. M. Stam, G. van Harten, J. de Boer, C. U. Keller, and ISPEX citizen scientists, “Mapping atmospheric aerosols with a citizen science network of smartphone spectropolarimeters,” Geophys. Res. Lett. 41, 7351–7358 (2014).
[Crossref]

Applegate, B.

H. Kim, Y. Jung, I.-J. Doh, R. A. Lozano-Mahecha, B. Applegate, and E. Bae, “Smartphone-based low light detection for bioluminescence application,” Sci. Reports 7, 40203 (2017).
[Crossref]

Aubé, M.

A. Sánchez de Miguel, C. C. Kyba, M. Aubé, J. Zamorano, N. Cardiel, C. Tapia, J. Bennie, and K. J. Gaston, “Colour remote sensing of the impact of artificial light at night (I): The potential of the International Space Station and other DSLR-based platforms,” Remote. Sens. Environ. 224, 92–103 (2019).
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A. Hänel, T. Posch, S. J. Ribas, M. Aubé, D. Duriscoe, A. Jechow, Z. Kollath, D. E. Lolkema, C. Moore, N. Schmidt, H. Spoelstra, G. Wuchterl, and C. C. Kyba, “Measuring night sky brightness: methods and challenges,” J. Quant. Spectrosc. Radiat. Transf. 205, 278–290 (2018).
[Crossref]

Bae, E.

H. Kim, Y. Jung, I.-J. Doh, R. A. Lozano-Mahecha, B. Applegate, and E. Bae, “Smartphone-based low light detection for bioluminescence application,” Sci. Reports 7, 40203 (2017).
[Crossref]

Bal, A.

A. Kordecki, A. Bal, and H. Palus, “A study of vignetting correction methods in camera colorimetric calibration,” in Proc. SPIE 10341, vol. 10341 (International Society for Optics and Photonics, 2017), p. 103410X.
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Balkunde, P.

S. Levin, S. Krishnan, S. Rajkumar, N. Halery, and P. Balkunde, “Monitoring of fluoride in water samples using a smartphone,” Sci. The Total. Environ. 551–552, 101–107 (2016).
[Crossref]

Bao, Y.

F. Li, Y. Bao, D. Wang, W. Wang, and L. Niu, “Smartphones for sensing,” Sci. Bull. 61, 190–201 (2016).
[Crossref]

Bayer, B. E.

B. E. Bayer, “Color imaging array,” (1975).

Bégué, A.

V. Lebourgeois, A. Bégué, S. Labbé, B. Mallavan, L. Prévot, and B. Roux, “Can commercial digital cameras be used as multispectral sensors? A crop monitoring test,” Sensors 8, 7300–7322 (2008).
[Crossref] [PubMed]

Beisl, U.

U. Beisl, “Absolute spectroradiometric calibration of the ADS40 sensor,” in Proc. of the ISPRS Commission I Symposium “From Sensors to Imagery”, Paris – Marne-la-Vallée, (2006).

Ben Dor, E.

S. Manfreda, M. McCabe, P. Miller, R. Lucas, V. Pajuelo Madrigal, G. Mallinis, E. Ben Dor, D. Helman, L. Estes, G. Ciraolo, J. Müllerová, F. Tauro, M. de Lima, J. de Lima, A. Maltese, F. Frances, K. Caylor, M. Kohv, M. Perks, G. Ruiz-Pérez, Z. Su, G. Vico, and B. Toth, “On the use of unmanned aerial systems for environmental monitoring,” Remote. Sens. 10, 641 (2018).
[Crossref]

Bennie, J.

A. Sánchez de Miguel, C. C. Kyba, M. Aubé, J. Zamorano, N. Cardiel, C. Tapia, J. Bennie, and K. J. Gaston, “Colour remote sensing of the impact of artificial light at night (I): The potential of the International Space Station and other DSLR-based platforms,” Remote. Sens. Environ. 224, 92–103 (2019).
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Berra, E.

E. Berra, S. Gibson-Poole, A. MacArthur, R. Gaulton, and A. Hamilton, “Estimation of the spectral sensitivity functions of un-modified and modified commercial off-the-shelf digital cameras to enable their use as a multispectral imaging system for UAVs,” in International Conference on Unmanned Aerial Vehicles in Geomatics, (2015), pp. 207–214.

Bing Kang, S.

Y. Zheng, S. Lin, C. Kambhamettu, J. Yu, and S. Bing Kang, “Single-image vignetting correction,” IEEE Transactions on Pattern Analysis Mach. Intell. 31, 2243–2256 (2009).
[Crossref]

Bisetty, K.

S. Kanchi, M. I. Sabela, P. S. Mdluli, Inamuddin, and K. Bisetty, “Smartphone based bioanalytical and diagnosis applications: A review,” Biosens. Bioelectron. 102, 136–149 (2018).
[Crossref]

Blumthaler, M.

A. Kreuter, C. Emde, and M. Blumthaler, “Measuring the influence of aerosols and albedo on sky polarization,” Atmospheric Res. 98, 363–367 (2010).
[Crossref]

A. Kreuter, M. Zangerl, M. Schwarzmann, and M. Blumthaler, “All-sky imaging: a simple, versatile system for atmospheric research,” Appl. Opt. 48, 1091–1097 (2009).
[Crossref] [PubMed]

Bongiorno, D. L.

D. L. Bongiorno, M. Bryson, D. G. Dansereau, and S. B. Williams, “Spectral characterization of COTS RGB cameras using a linear variable edge filter,” in Proc. SPIE 8660, Digital Photography IX, N. Sampat and S. Battiato, eds. (International Society for Optics and Photonics, 2013), p. 86600N.

Boss, E.

T. Leeuw and E. Boss, “The HydroColor app: Above water measurements of remote sensing reflectance and turbidity using a smartphone camera,” Sensors 18, 256 (2018).
[Crossref]

Boudreaux, J.

R. G. Mannino, D. R. Myers, E. A. Tyburski, C. Caruso, J. Boudreaux, T. Leong, G. D. Clifford, and W. A. Lam, “Smartphone app for non-invasive detection of anemia using only patient-sourced photos,” Nat. Commun. 9, 4924 (2018).
[Crossref] [PubMed]

Bowers, D. G.

L. Goddijn-Murphy, D. Dailloux, M. White, and D. G. Bowers, “Fundamentals of in situ digital camera methodology for water quality monitoring of coast and ocean,” Sensors 9, 5825–5843 (2009).
[Crossref] [PubMed]

Brodie, K.

D. Whiteson, M. Mulhearn, C. Shimmin, K. Cranmer, K. Brodie, and D. Burns, “Searching for ultra-high energy cosmic rays with smartphones,” Astropart. Phys. 79, 1–9 (2016).
[Crossref]

Brown, M. S.

R. Nguyen, D. K. Prasad, and M. S. Brown, “Raw-to-raw: Mapping between image sensor color responses,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2014), pp. 3398–3405.

Brown, S. W.

S. W. Brown, T. C. Larason, C. Habauzit, G. P. Eppeldauer, Y. Ohno, and K. R. Lykke, “Absolute radiometric calibration of digital imaging systems,” in Proc. SPIE 4306, vol. 4306M. M. Blouke, J. Canosa, and N. Sampat, eds. (International Society for Optics and Photonics, 2001).

Bryson, M.

D. L. Bongiorno, M. Bryson, D. G. Dansereau, and S. B. Williams, “Spectral characterization of COTS RGB cameras using a linear variable edge filter,” in Proc. SPIE 8660, Digital Photography IX, N. Sampat and S. Battiato, eds. (International Society for Optics and Photonics, 2013), p. 86600N.

Burns, D.

D. Whiteson, M. Mulhearn, C. Shimmin, K. Cranmer, K. Brodie, and D. Burns, “Searching for ultra-high energy cosmic rays with smartphones,” Astropart. Phys. 79, 1–9 (2016).
[Crossref]

Busch, J. A.

A. Friedrichs, J. A. Busch, H. J. van der Woerd, and O. Zielinski, “SmartFluo: A method and affordable adapter to measure chlorophyll a fluorescence with smartphones,” Sensors 17, 678 (2017).
[Crossref]

Cai, F.

F. Cai, W. Lu, W. Shi, and S. He, “A mobile device-based imaging spectrometer for environmental monitoring by attaching a lightweight small module to a commercial digital camera,” Sci. Reports 7, 15602 (2017).
[Crossref]

Calvagno, G.

D. Menon, S. Andriani, and G. Calvagno, “Demosaicing with directional filtering and a posteriori decision,” IEEE Transactions on Image Process. 16, 132–141 (2007).
[Crossref]

Cardiel, N.

A. Sánchez de Miguel, C. C. Kyba, M. Aubé, J. Zamorano, N. Cardiel, C. Tapia, J. Bennie, and K. J. Gaston, “Colour remote sensing of the impact of artificial light at night (I): The potential of the International Space Station and other DSLR-based platforms,” Remote. Sens. Environ. 224, 92–103 (2019).
[Crossref]

Carter, B.

D. P. Igoe, A. V. Parisi, and B. Carter, “A method for determining the dark response for scientific imaging with smartphones,” Instrumentation Sci. Technol. 42, 586–592 (2014).
[Crossref]

Caruso, C.

R. G. Mannino, D. R. Myers, E. A. Tyburski, C. Caruso, J. Boudreaux, T. Leong, G. D. Clifford, and W. A. Lam, “Smartphone app for non-invasive detection of anemia using only patient-sourced photos,” Nat. Commun. 9, 4924 (2018).
[Crossref] [PubMed]

Cavallo, F.

G. Rateni, P. Dario, and F. Cavallo, “Smartphone-based food diagnostic technologies: A review,” Sensors 17, 1453 (2017).
[Crossref]

Caylor, K.

S. Manfreda, M. McCabe, P. Miller, R. Lucas, V. Pajuelo Madrigal, G. Mallinis, E. Ben Dor, D. Helman, L. Estes, G. Ciraolo, J. Müllerová, F. Tauro, M. de Lima, J. de Lima, A. Maltese, F. Frances, K. Caylor, M. Kohv, M. Perks, G. Ruiz-Pérez, Z. Su, G. Vico, and B. Toth, “On the use of unmanned aerial systems for environmental monitoring,” Remote. Sens. 10, 641 (2018).
[Crossref]

Cazenavette, G.

M. Pagnutti, R. E. Ryan, G. Cazenavette, M. Gold, R. Harlan, E. Leggett, and J. Pagnutti, “Laying the foundation to use Raspberry Pi 3 V2 camera module imagery for scientific and engineering purposes,” J. Electron. Imaging 26, 013014 (2017).
[Crossref]

Cescatti, A.

A. Cescatti, “Indirect estimates of canopy gap fraction based on the linear conversion of hemispherical photographs: Methodology and comparison with standard thresholding techniques,” Agric. For. Meteorol. 143, 1–12 (2007).
[Crossref]

Chaitavon, K.

S. Sumriddetchkajorn, K. Chaitavon, and Y. Intaravanne, “Mobile-platform based colorimeter for monitoring chlorine concentration in water,” Sensors Actuators B: Chem. 191, 561–566 (2014).
[Crossref]

Chaji, S.

Chang, Y.-C.

L.-J. Wang, Y.-C. Chang, R. Sun, and L. Li, “A multichannel smartphone optical biosensor for high-throughput point-of-care diagnostics,” Biosens. Bioelectron. 87, 686–692 (2017).
[Crossref]

Chapra, S.

K. Flynn and S. Chapra, “Remote sensing of submerged aquatic vegetation in a shallow non-turbid river using an unmanned aerial vehicle,” Remote. Sens. 6, 12815–12836 (2014).
[Crossref]

Charrière, R.

Chen, C.

H. Ding, C. Chen, S. Qi, C. Han, and C. Yue, “Smartphone-based spectrometer with high spectral accuracy for mHealth application,” Sensors Actuators A: Phys. 274, 94–100 (2018).
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Chen, J.

X. Huang, D. Xu, J. Chen, J. Liu, Y. Li, J. Song, X. Ma, and J. Guo, “Smartphone-based analytical biosensors,” Analyst 143, 5339–5351 (2018).
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Chen, J. F.

Chen, Z. T.

Cheng, G.

C. Zhang, G. Cheng, P. Edwards, M.-D. Zhou, S. Zheng, and Z. Liu, “G-Fresnel smartphone spectrometer,” Lab on a Chip 16, 246–250 (2016).
[Crossref]

Chernouss, S.

Chesnokov, V.

V. D. Silva, V. Chesnokov, and D. Larkin, “A novel adaptive shading correction algorithm for camera systems,” Electron. Imaging 2016, 1–5 (2016).
[Crossref]

Cheung, V.

Christensen, S.

J. Rasmussen, G. Ntakos, J. Nielsen, J. Svensgaard, R. N. Poulsen, and S. Christensen, “Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?” Eur. J. Agron. 74, 75–92 (2016).
[Crossref]

Chu, K.

Z. J. Smith, K. Chu, A. R. Espenson, M. Rahimzadeh, A. Gryshuk, M. Molinaro, D. M. Dwyre, S. Lane, D. Matthews, and S. Wachsmann-Hogiu, “Cell-phone-based platform for biomedical device development and education applications,” PLOS ONE 6, e17150 (2011).
[Crossref] [PubMed]

Chuan, C. H.

J. B. Gallagher and C. H. Chuan, “Chlorophyll a and turbidity distributions: Applicability of using a smartphone app across two contrasting bays,” J. Coast. Res. 34, 1236–1243 (2018).
[Crossref]

Chung, K.-L.

C.-H. Lin, K.-L. Chung, and C.-W. Yu, “Novel chroma subsampling strategy based on mathematical optimization for compressing mosaic videos with arbitrary RGB color filter arrays in H.264/AVC and HEVC,” IEEE Transactions on Circuits Syst. for Video Technol. 26, 1722–1733 (2016).
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Chung, Y.

W. Yu, Y. Chung, and J. Soh, “Vignetting distortion correction method for high quality digital imaging,” in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., (IEEE, 2004), pp. 666–669.

Ciraolo, G.

S. Manfreda, M. McCabe, P. Miller, R. Lucas, V. Pajuelo Madrigal, G. Mallinis, E. Ben Dor, D. Helman, L. Estes, G. Ciraolo, J. Müllerová, F. Tauro, M. de Lima, J. de Lima, A. Maltese, F. Frances, K. Caylor, M. Kohv, M. Perks, G. Ruiz-Pérez, Z. Su, G. Vico, and B. Toth, “On the use of unmanned aerial systems for environmental monitoring,” Remote. Sens. 10, 641 (2018).
[Crossref]

Clifford, G. D.

R. G. Mannino, D. R. Myers, E. A. Tyburski, C. Caruso, J. Boudreaux, T. Leong, G. D. Clifford, and W. A. Lam, “Smartphone app for non-invasive detection of anemia using only patient-sourced photos,” Nat. Commun. 9, 4924 (2018).
[Crossref] [PubMed]

Coburn, C. A.

C. A. Coburn, A. M. Smith, G. S. Logie, and P. Kennedy, “Radiometric and spectral comparison of inexpensive camera systems used for remote sensing,” Int. J. Remote. Sens. 39, 4869–4890 (2018).
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Connah, D.

Cook, J. M.

A. J. S. McGonigle, T. C. Wilkes, T. D. Pering, J. R. Willmott, J. M. Cook, F. M. Mims, and A. V. Parisi, “Smartphone spectrometers,” Sensors 18, 1–15 (2018).
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K. D. Long, E. V. Woodburn, H. M. Le, U. K. Shah, S. S. Lumetta, and B. T. Cunningham, “Multimode smartphone biosensing: the transmission, reflection, and intensity spectral (TRI)-analyzer,” Lab on a Chip 17, 3246–3257 (2017).
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Wuchterl, G.

A. Hänel, T. Posch, S. J. Ribas, M. Aubé, D. Duriscoe, A. Jechow, Z. Kollath, D. E. Lolkema, C. Moore, N. Schmidt, H. Spoelstra, G. Wuchterl, and C. C. Kyba, “Measuring night sky brightness: methods and challenges,” J. Quant. Spectrosc. Radiat. Transf. 205, 278–290 (2018).
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X. Huang, D. Xu, J. Chen, J. Liu, Y. Li, J. Song, X. Ma, and J. Guo, “Smartphone-based analytical biosensors,” Analyst 143, 5339–5351 (2018).
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Xu, Y. Q.

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Y. Wang, M. M. A. Zeinhom, M. Yang, R. Sun, S. Wang, J. N. Smith, C. Timchalk, L. Li, Y. Lin, and D. Du, “A 3D-printed, portable, optical-sensing platform for smartphones capable of detecting the herbicide 2,4-dichlorophenoxyacetic acid,” Anal. Chem. 89, 9339–9346 (2017).
[Crossref] [PubMed]

Yang, P.-K.

P.-K. Yang, “Determining the spectral responsivity from relative measurements by using multicolor light-emitting diodes as probing light sources,” Optik 126, 3088–3092 (2015).
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Yang, Y.

Y. Yang, L. L. Cowen, and M. Costa, “Is ocean reflectance acquired by citizen scientists robust for science applications?” Remote. Sens. 10, 835 (2018).
[Crossref]

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A. A. Zaidan, B. B. Zaidan, O. S. Albahri, M. A. Alsalem, A. S. Albahri, Q. M. Yas, and M. Hashim, “A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution,” Heal. Technol. 8, 223–238 (2018).
[Crossref]

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K. E. McCracken and J.-Y. Yoon, “Recent approaches for optical smartphone sensing in resource-limited settings: a brief review,” Anal. Methods 8, 6591–6601 (2016).
[Crossref]

Yu, C.-W.

C.-H. Lin, K.-L. Chung, and C.-W. Yu, “Novel chroma subsampling strategy based on mathematical optimization for compressing mosaic videos with arbitrary RGB color filter arrays in H.264/AVC and HEVC,” IEEE Transactions on Circuits Syst. for Video Technol. 26, 1722–1733 (2016).
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Yue, C.

H. Ding, C. Chen, S. Qi, C. Han, and C. Yue, “Smartphone-based spectrometer with high spectral accuracy for mHealth application,” Sensors Actuators A: Phys. 274, 94–100 (2018).
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A. A. Zaidan, B. B. Zaidan, O. S. Albahri, M. A. Alsalem, A. S. Albahri, Q. M. Yas, and M. Hashim, “A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution,” Heal. Technol. 8, 223–238 (2018).
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A. A. Zaidan, B. B. Zaidan, O. S. Albahri, M. A. Alsalem, A. S. Albahri, Q. M. Yas, and M. Hashim, “A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution,” Heal. Technol. 8, 223–238 (2018).
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C. E. Tapia Ayuga and J. Zamorano, “LICA AstroCalc, a software to analyze the impact of artificial light: Extracting parameters from the spectra of street and indoor lamps,” J. Quant. Spectrosc. Radiat. Transf. 214, 33–38 (2018).
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Anal. Methods (1)

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Figures (11)

Fig. 1
Fig. 1 Setup used to measure linearity, ISO-gain relations, and inter-pixel gain variations on smartphones. The first linear polarizer was rotatable, the second fixed. Smartphones were placed with their camera flush against the view-port at the top of the integrating sphere.
Fig. 2
Fig. 2 Histogram of Pearson r coefficients for RAW (black, all filters combined) and JPEG (red/green/blue) responses. The r ≥ 0.980 cut-off is shown with a dashed black line. The respective cameras are listed next to the vertical axis. Note the logarithmic vertical scale.
Fig. 3
Fig. 3 JPEG (blue, left vertical axis) and RAW (black, right axis) response of a single B pixel in the iPhone SE (left) and Galaxy S8 (right) rear cameras, under varying incident intensities. Each point represents the mean of a stack of 10 images at the same exposure. Vertical error bars are smaller than the dot size. The black and blue lines represent the best-fitting linear (RAW) and sRGB-like (JPEG) profiles, respectively. The lower row shows the residuals, normalized to the dynamic range.
Fig. 4
Fig. 4 Histogram of best-fitting γ and RMS relative difference between JPEG data and fit (for models with γ = 2.2 and 2.4) in the RGB bands.
Fig. 5
Fig. 5 Read-out noise per pixel of two iPhone SE devices (top and bottom) at ISO speed 23, in the RGBG2 filters from left to right. Darker colors correspond to lower read-out noise. A two-dimensional Gaussian filter (σ = 5 pixels) has been applied to better visualize large-scale variations. The G image shows similar patterns to Fig. 7.
Fig. 6
Fig. 6 ISO speed normalization for the iPhone SE, Samsung Galaxy S8, and Nikon D5300. Dots indicate means of image stacks divided by the mean value per pixel at the lowest ISO speed. Lines indicate the best-fitting relationships.
Fig. 7
Fig. 7 Gain values of G pixels in the iPhone SE (left; ISO speed 88) and Galaxy S8 (right; ISO speed 200) sensors. Darker colors indicate lower gain values. A two-dimensional Gaussian filter (σ = 5) has been applied to better visualize large-scale fluctuations. The iPhone SE patterns are similar to the read noise shown in Fig. 5.
Fig. 8
Fig. 8 Histogram of gain values in the R (top), G and G2 (middle), and B pixels (bottom) in the iPhone SE (left; ISO speed 88) and Galaxy S8 (right; ISO speed 200) sensors. The vertical axes were normalized to account for the different amounts of pixels.
Fig. 9
Fig. 9 Flat-field correction factor g for the iPhone SE camera. From left to right: observed values (inverse of observed relative sensitivity), best-fitting DNG model, and residuals.
Fig. 10
Fig. 10 Spectral response curves of the iPhone SE, Galaxy S8, and Phantom Pro 4, derived from monochromator data. The responses are normalized to the global maximum per camera, giving relative sensitivities. G is the average of the G and G2 responses over the wavelength axis, since no significant differences were found. RMS errors are ≤0.005.
Fig. 11
Fig. 11 Comparison of the iPhone SE spectral response curves measured with the monochromator and iSPEX. iSPEX data are normalized using a 5777 K black-body and a SMARTS2 model, as described in Sect. 3.9.

Tables (1)

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Table 1 Peak response wavelength λP,�� and effective spectral bandwidth Λ�� of each filter in the three cameras, derived from monochromator measurements. All values are in nm.

Equations (7)

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J 𝒞 = 255 × { 12.92 n I if n I < 0.0031308 1.055 ( n I ) 1 / γ 0.055 otherwise
M = IG + DG + B
V = IG 2 + DG 2 + RON 2
V = GM cor + RON 2
g ( x , y ) = 1 + k 0 r 2 + k 1 r 4 + k 2 r 6 + k 3 r 8 + k 4 r 10
L 𝒞 = hc 1 A d Λ 𝒞 g [ 4 ( f # ) 2 π τ N ] ( M B D τ )
L 𝒞 , λ = h c λ 1 A d R 𝒞 ( λ ) Δ λ g [ 4 ( f # ) 2 π τ N ] ( M λ B D τ )