Skip to main content
SpringerLink
Log in

I3D: a new dataset for testing denoising and demosaicing algorithms

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper we present a dataset of images to test the performance of image processing algorithms, in particular demosaicing and denoising methods. Despite the plethora of demosaicing and denoising algorithms present in the literature, only few benchmarks are available to test their performance, and most of them are quite old, thus inadequate to represent the images captured by modern devices. The proposed dataset is composed by twenty 16 bit-depth images that can be used to test full-reference image quality metrics. More specifically, twelve pictures have been synthetically created by means of 2D or 3D softwares, while eight images have been captured by a high-end digital camera.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. To check the dark bias, a picture with a 1/8000 sec of exposure time, and the cap in front of the lens was taken. For the white point, we took a picture of a light source, in particular a lamp big enough to cover almost all the viewing area, for 15 sec, to be sure to saturate the sensor.

  2. These operations are needed to take account of the two pedestals, and to transform the linear raw values in sRGB.

References

  1. Adams JE Jr (1995) Interactions between color plane interpolation and other image processing functions in electronic photography. In: IS&T/SPIE’s Symposium on electronic imaging: science & technology. International Society for Optics and Photonics, pp 144–151

  2. Adobe (2017) http://www.adobe.com/products/illustrator.html

  3. Adobe (2017) http://www.photoshop.com/products

  4. Akiyama H, Tanaka M, Okutomi M (2015) Pseudo four-channel image denoising for noisy cfa raw data. In: 2015 IEEE International Conference on image processing (ICIP). IEEE, pp 4778–4782

  5. Assembly IR (2003) Methodology for the subjective assessment of the quality of television pictures. International Telecommunication Union

  6. Baek M, Jeong J (2014) Demosaicing algorithm using high-order interpolation with sobel operators. In: Proc. of the World Congress on engineering and computer science

  7. Baraniuk R (1999) Optimal tree approximation with wavelets. In: SPIE’s International symposium on optical science, engineering, and instrumentation. International Society for Optics and Photonics, pp 196–207

  8. Blender (2017) https://www.blender.org/

  9. Buades A, Coll B, Morel JM (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4(2):490–530

    Article  MathSciNet  MATH  Google Scholar 

  10. Buccigrossi RW, Simoncelli EP (1999) Image compression via joint statistical characterization in the wavelet domain. IEEE Trans Image Process 8(12):1688–1701

    Article  Google Scholar 

  11. Bui TD, Chen G (1998) Translation-invariant denoising using multiwavelets. IEEE Trans Signal Process 46(12):3414–3420

    Article  Google Scholar 

  12. Choi H, Baraniuk R (1998) Analysis of wavelet-domain wiener filters. In: Proceedings of the IEEE-SP International symposium on time-frequency and time-scale analysis, 1998. IEEE, pp 613–616

  13. Chung KH, Chan YH (2006) Color demosaicing using variance of color differences. IEEE Trans Image Process 15(10):2944–2955

    Article  Google Scholar 

  14. Chung KH, Chan YH (2010) Low-complexity color demosaicing algorithm based on integrated gradients. J Electr Imaging 19(2):021104–021104

    Article  Google Scholar 

  15. Coffin D (2017) https://www.cybercom.net/~dcoffin/dcraw/

  16. Cohen I, Raz S, Malah D (1999) Translation-invariant denoising using the minimum description length criterion. Signal Process 75(3):201–223

    Article  MATH  Google Scholar 

  17. Condat L, Mosaddegh S (2012) Joint demosaicking and denoising by total variation minimization. In: 2012 19th IEEE International conference on image processing. IEEE, pp 2781–2784

  18. Condat L. (2016) http://www.gipsa-lab.grenoble-inp.fr/%7elaurent.condat/imagebase.html

  19. Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41 (3):613–627

    Article  MathSciNet  MATH  Google Scholar 

  20. Donoho DL, Johnstone JM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455

    Article  MathSciNet  MATH  Google Scholar 

  21. DPreview (2017) https://www.dpreview.com/reviews/canon-50-1p4-c16/5

  22. Duran J, Buades A (2015) A demosaicking algorithm with adaptive inter-channel correlation. Image Process Line 5:311–327. https://doi.org/10.5201/ipol.2015.145. http://demo.ipol.im/demo/145/

    Article  MathSciNet  Google Scholar 

  23. Eskicioglu AM, Fisher PS (1995) Image quality measures and their performance. IEEE Trans Commun 43(12):2959–2965

    Article  Google Scholar 

  24. Fodor IK, Kamath C (2003) Denoising through wavelet shrinkage: an empirical study. J Electr Imag 12(1):151–160

    Article  Google Scholar 

  25. Froment J (2014) Parameter-free fast pixelwise non-local means denoising. Image Process Line 4:300–326. https://doi.org/10.5201/ipol.2014.120. http://www.ipol.im/pub/art/2014/120/

    Article  Google Scholar 

  26. Gao D, Wu X, Shi G, Zhang L (2012) Color demosaicking with an image formation model and adaptive pca. J Vis Commun Image Represent 23(7):1019–1030

    Article  Google Scholar 

  27. Gharbi M, Chaurasia G, Paris S, Durand F (2016) Deep joint demosaicking and denoising. ACM Trans Graph (TOG) 35(6):191

    Article  Google Scholar 

  28. Go J, Sohn K, Lee C (2000) Interpolation using neural networks for digital still cameras. IEEE Trans Consum Electron 46(3):610–616

    Article  Google Scholar 

  29. Gonzalez RC, Richard E (2008) Woods, digital image processing. Prentice Hall Press, Pearson

    Google Scholar 

  30. Goossens B, Aelterman J, Luong H, Pižurica A, Philips W (2013) Complex wavelet joint denoising and demosaicing using gaussian scale mixtures. In: 2013 IEEE International conference on image processing. IEEE, pp 445–448

  31. Hamza AB, Luque-Escamilla PL, Martínez-Aroza J, Román-Roldán R. (1999) Removing noise and preserving details with relaxed median filters. J. Math. Imaging Vis. 11(2):161–177

    Article  MathSciNet  Google Scholar 

  32. Hibbard RH (1995) Apparatus and method for adaptively interpolating a full color image utilizing luminance gradients. US Patent 5,382,976

  33. Hirakawa K, Parks TW (2006) Joint demosaicing and denoising. IEEE Trans Image Process 15(8):2146–2157

    Article  Google Scholar 

  34. Hyvarinen A, Oja E, Hoyer P, Hurri J (1998) Image feature extraction by sparse coding and independent component analysis. In: Fourteenth International conference on pattern recognition, 1998. Proceedings, vol 2. IEEE, pp 1268–1273

  35. Jain P, Tyagi V (2016) A survey of edge-preserving image denoising methods. Inf Syst Front 18(1):159–170

    Article  Google Scholar 

  36. John DM, Thomas A (2015) Combined denoising and demosaicing of cfa images. In: 2015 IEEE International Conference on signal processing, informatics, communication and energy systems (SPICES). IEEE, pp 1–6

  37. Jung A (2001) An introduction to a new data analysis tool: independent component analysis. In: Proceedings of Workshop GK “Nonlinearity”. Regensburg

  38. Kakarala R, Baharav Z (2002) Adaptive demosaicing with the principal vector method. IEEE Trans Consum Electron 48(4):932–937

    Article  Google Scholar 

  39. Khashabi D, Nowozin S, Jancsary J, Fitzgibbon AW (2014) Joint demosaicing and denoising via learned nonparametric random fields. IEEE Trans Image Process 23 (12):4968–4981

    Article  MathSciNet  MATH  Google Scholar 

  40. Kimmel R (1999) Demosaicing: image reconstruction from color ccd samples. IEEE Trans Image Process 8(9):1221–1228

    Article  Google Scholar 

  41. Klatzer T, Hammernik K, Knobelreiter P, Pock T (2016) Learning joint demosaicing and denoising based on sequential energy minimization. In: 2016 IEEE International Conference on computational photography (ICCP). IEEE, pp 1–11

  42. Kodac - Bradley J. (2016) Lucier webpage: https://www.math.purdue.edu/~lucier/PHOTO_CD/RIGHTS/RIGHTS.USE

  43. Kodac - Rich Franzen webpage: (2016) http://r0k.us/graphics/kodak/

  44. Lang M, Guo H, Odegard JE, Burrus CS, Wells RO Jr (1995) Nonlinear processing of a shift-invariant discrete wavelet transform (dwt) for noise reduction. In: SPIE’s 1995 Symposium on OE/aerospace sensing and dual use photonics. International Society for Optics and Photonics, pp 640–651

  45. Laroche CA, Prescott MA (1994) Apparatus and method for adaptively interpolating a full color image utilizing chrominance gradients. US Patent 5,373,322

  46. Lebrun M, Buades A, Morel JM (2013) Implementation of the “Non-Local Bayes” (NL-Bayes) image denoising algorithm. Image Process Line 3:1–42. https://doi.org/10.5201/ipol.2013.16

    Article  Google Scholar 

  47. Lebrun M, Colom M, Morel JM (2015) The noise clinic: a blind image denoising algorithm. Image Process Line 5:1–54. https://doi.org/10.5201/ipol.2015.125. http://demo.ipol.im/demo/125/

    Article  MATH  Google Scholar 

  48. Lee K, Jeong S, Choi Js, Lee S (2014) Multiscale edge-guided demosaicking algorithm. In: The 18th IEEE International symposium on consumer electronics (ISCE 2014). IEEE, pp 1–3

  49. Lian NX, Chang L, Tan YP, Zagorodnov V (2007) Adaptive filtering for color filter array demosaicking. IEEE Trans Image Process 16(10):2515–2525

    Article  MathSciNet  Google Scholar 

  50. Lu J, Weaver JB, Healy DM, Xu Y (1992) Noise reduction with a multiscale edge representation and perceptual criteria. In: Proceedings of the IEEE-SP international symposium on time-frequency and time-scale analysis, 1992. IEEE, pp 555–558

  51. Menon D, Calvagno G (2007) Demosaicing based onwavelet analysis of the luminance component. In: 2007 IEEE International conference on image processing, vol 2. IEEE, pp II–181

  52. Menon D, Calvagno G (2009) Joint demosaicking and denoisingwith space-varying filters. In: 2009 16th IEEE International conference on image processing (ICIP). IEEE, pp 477–480

  53. Motwani MC, Gadiya MC, Motwani RC, Harris FC (2004) Survey of image denoising techniques. In: Proceedings of GSPX, pp 27–30

  54. Nuno-Maganda MA, Arias-Estrada MO (2005) Real-time fpga-based architecture for bicubic interpolation: an application for digital image scaling. In: 2005 International Conference on reconfigurable computing and FPGAs (ReConFig’05). IEEE, pp 8–pp

  55. Online IP (2018) http://www.ipol.im/

  56. Paliy D, Foi A, Bilcu R, Katkovnik V (2008) Denoising and interpolation of noisy bayer data with adaptive cross-color filters. In: Electronic Imaging 2008. International Society for Optics and Photonics, pp 68221K–68221K

  57. Park J, Jang ES, Chong JW (2016) Demosaicing method for digital cameras with white-rgb color filter array. ETRI J 38(1):164–173

    Article  Google Scholar 

  58. Pei SC, Tam IK (2003) Effective color interpolation in ccd color filter arrays using signal correlation. IEEE Trans Circ Syst Vid Technol 13(6):503–513

    Article  Google Scholar 

  59. Pekkucuksen I, Altunbasak Y (2013) Multiscale gradients-based color filter array interpolation. IEEE Trans Image Process 22(1):157–165

    Article  MathSciNet  MATH  Google Scholar 

  60. Pierazzo N, Facciolo G (2017) Data adaptive dual domain denoising: a method to boost state of the art denoising algorithms. Image Process Line 7:93–114. https://doi.org/10.5201/ipol.2017.203. http://www.ipol.im/pub/art/2017/203/

    Article  Google Scholar 

  61. Planetside (2017) http://planetside.co.uk/

  62. Portilla J, Strela V, Wainwright MJ, Simoncelli EP (2003) Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Trans Image Process 12 (11):1338–1351

    Article  MathSciNet  MATH  Google Scholar 

  63. Prakash VS, Prasad KS, Prasad TJC (2016) Color image demosaicing using sparse based radial basis function network. Alexandria Eng J

  64. Preethi S, Narmadha D (2012) A survey on image denoising techniques. Int J Comput Appl, 58(6)

  65. Rajaei B (2014) An analysis and improvement of the BLS-GSM denoising method. Image Process Line 4:44–70. https://doi.org/10.5201/ipol.2014.86. http://www.ipol.im/pub/art/2014/86/

    Article  Google Scholar 

  66. Rizzi A, Algeri T, Medeghini G, Marini D (2004) A proposal for contrast measure in digital images. In: CGIV 2004 - Second European conference on color in graphics, imaging, and vision and sixth international symposium on multispectral color science. Aachen, pp 187–192

  67. Romberg JK, Choi H, Baraniuk R (2001) Bayesian tree-structured image modeling using wavelet-domain hidden Markov models. IEEE Trans Image Process 10 (7):1056–1068

    Article  Google Scholar 

  68. Shao L, Rehman AU (2014) Image demosaicing using content and colour-correlation analysis. Signal Process 103:84–91

    Article  Google Scholar 

  69. Strela V (2001) Denoising via block wiener filtering in wavelet domain. In: European Congress of mathematics. Springer, pp 619–625

  70. Su CY (2006) Highly effective iterative demosaicing using weighted-edge and color-difference interpolations. IEEE Trans Consum Electron 52(2):639–645

    Article  Google Scholar 

  71. Su CY (2017) http://web.ntnu.edu.tw/~scy/heid_demo.html

  72. Sung DC, Tsao HW (2015) Demosaicing using subband-based classifiers. Electron Lett 51(3):228–230

    Article  Google Scholar 

  73. Tao B, Tastl I, Cooper T, Blasgen M, Edwards E (1999) Demosaicing using human visual properties and wavelet interpolation filtering. In: Color and imaging conference, vol 1999. Society for Imaging Science and Technology, pp 252–256

  74. Thung KH, Raveendran P (2009) A survey of image quality measures. In: 2009 International Conference for technical postgraduates (TECHPOS). IEEE, pp 1–4

  75. Wang Z, Simoncelli EP (2005) Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Human Vision and electronic imaging, vol 5666, pp 149–159

  76. Wang YQ, Limare N (2015) A fast C++ implementation of neural network backpropagation training algorithm: application to Bayesian optimal image demosaicing. Image Process Line 5:257–266. https://doi.org/10.5201/ipol.2015.137. http://demo.ipol.im/demo/137/

    Article  Google Scholar 

  77. Wang J, Wu J, Wu Z, Jeon G (2017) Filter-based bayer pattern cfa demosaicking. Circ, Syst, Signal Process, 1–24

  78. Wiener N (1949) Extrapolation, interpolation, and smoothing of stationary time series, vol 2. MIT Press, Cambridge

    Book  MATH  Google Scholar 

  79. Wu J, Anisetti M, Wu W, Damiani E, Jeon G (2016) Bayer demosaicking with polynomial interpolation. IEEE Trans Image Process 25(11):5369–5382

    Article  MathSciNet  MATH  Google Scholar 

  80. Wu J, Timofte R, Van Gool L (2016) Demosaicing based on directional difference regression and efficient regression priors IEEE transactions on image processing: a publication of the IEEE signal processing society

  81. Yang R, Yin L, Gabbouj M, Astola J, Neuvo Y (1995) Optimal weighted median filtering under structural constraints. IEEE Trans Signal Process 43(3):591–604

    Article  Google Scholar 

  82. Yang B, Luo J, Guo L, Cheng F (2016) Simultaneous image fusion and demosaicing via compressive sensing. Inf Process Lett 116(7):447–454

    Article  MathSciNet  MATH  Google Scholar 

  83. Zhang (2016) http://www4.comp.polyu.edu.hk/~cslzhang/CDM_Dataset.htm

  84. Zhang L, Wu X (2005) Color demosaicking via directional linear minimum mean square-error estimation. IEEE Trans Image Process 14(12):2167–2178

    Article  Google Scholar 

  85. Zhang L, Wu X (2017) http://www4.comp.polyu.edu.hk/~cslzhang/PCA-CFA-Denoising.htm

  86. Zhang H, Nosratinia A, Wells R (2000) Image denoising via wavelet-domain spatially adaptive fir wiener filtering. In: 2000 IEEE International Conference on acoustics, speech, and signal processing, 2000. ICASSP’00. Proceedings, vol 6. IEEE, pp 2179–2182

  87. Zhang L, Lukac R, Wu X, Zhang D (2009) Pca-based spatially adaptive denoising of cfa images for single-sensor digital cameras. IEEE Trans Image Process 18 (4):797–812

    Article  MathSciNet  MATH  Google Scholar 

  88. Zhang L, Wu X, Buades A, Li X (2011) Color demosaicking by local directional interpolation and nonlocal adaptive thresholding. J Electron Imaging 20 (2):023016–023016

    Article  Google Scholar 

  89. Zhang C, Li Y, Wang J, Hao P (2016) Universal demosaicking of color filter arrays. IEEE Trans Image Process 25(11):5173–5186

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

We would like to thank John McCann, Prof. Bradley Lucier and Prof. Michael Kriss, that answered to our questions about the Kodak Photo CD.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Milan, via Comelico 39, 20135, Milan, Italy

    Cristian Bonanomi, Simone Balletti, Marco Anisetti & Alessandro Rizzi

  2. Fondazione Bruno Kessler, Center for Information and Communication Technology, via Sommarive 18, 38123, Trento, Italy

    Michela Lecca

  3. ETISALAT BT Innovation Center Khalifa University, Abu Dhabi, United Arab Emirates

    Ernesto Damiani

Authors
  1. Cristian Bonanomi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simone Balletti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michela Lecca
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco Anisetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alessandro Rizzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ernesto Damiani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristian Bonanomi.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was partially supported by the “Ministero degli Affari Esteri e della Cooperazione Internazionale” of Italy under Grant PGR00217.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bonanomi, C., Balletti, S., Lecca, M. et al. I3D: a new dataset for testing denoising and demosaicing algorithms. Multimed Tools Appl 79, 8599–8626 (2020). https://doi.org/10.1007/s11042-018-6396-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6396-4

Keywords

Search

Navigation