False-color image processing

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Introduction[edit | edit source]

False color image processing is a non-invasive technique that combines and rearranges the color channels from one or multiple source images that results in a final composite image. The colors rendered in this resulting image do not match those that would be observed naturally by the human eye. These methods help visualize information not otherwise discernable which can aid in interpretation of media and materials present and inform further scientific analysis, conservation examination, and scholarly research.

Within cultural heritage, the most common use of false color image processing is as a derivative of infrared (IR) or ultraviolet (UV) photography. These methods rearrange the Red, Green and Blue channels of a visible-color image and insert an infrared or ultraviolet capture. Processing methods have expanded with the growing popularity of spectral imaging and the adaptation of remote sensing analyses. These techniques, which include principal component analysis (PCA), derive from a data cube of registered images captured at various wavebands from the ultraviolet, visible, and infrared spectrums.

History and background[edit | edit source]

Before digital photography became more widespread, conservators and photographers could only rely on infrared photographic emulsions, among which the Kodak Ektachrome infrared film type 2236. Its three emulsion layers were sensitized to infrared (in the range between 750 nm and 900 nm), red and green wavebands respectively. It is therefore here that the traditional color arrangement for the infrared false color was born, and the same result is often sought after with digital image processing.

The earliest use of infrared false color photography in the cultural field is reported in the late 1970s, where pigments, inks and glasses were first examined, but it was only in the 1990s that an attempt of standard procedure was proposed[1].

Definitions[edit | edit source]

Techniques[edit | edit source]

The aim of using false color processing techniques (and their derivatives) is to enhance the signal recorded in the source images, which commonly span through the UV-NIR range of the electromagnetic spectrum.

These methods  can be applied to almost every type of cultural heritage object. However, it should be noted that pigments and materials may be overlayed, mixed, or their recipes vary. Therefore false color processing is not meant to selectively identify media or materials but rather to suggest what may be present.

Depending on which technique is used and the object’s constituting materials, it is possible to investigate the object’s characteristics in relation to the research scope, such as:

  • Qualitative identification of pigments, inks and dyes
  • Discrimination of metameric pigments, inks and dyes
  • Location and mapping of previous integrations and treatments
  • Enhance discoloured or faded features of the support, the text or the polychromy
  • Separate / distinguish layers (of paint, text, etc.)
  • Identify subtle differences within a single material
  • Enhance surface texture to assess construction techniques (e.g. woodcuts, compass holes, carvings, indented handwriting, etc.)
  • Enhance watermarks
  • Assess substrate density
  • Non-invasively reveal information beneath pastedowns
  • Track changes over time (of material degradation, before/after exhibition, before/after treatment, artificial aging of scientific samples, etc.)

From single capture[edit | edit source]

False Color Infrared (FCIR)[edit | edit source]
Infrared False Colour of a painting (750 nm 850 nm)
Visible light photography of a discoloured painting (top), the infrared image captured with a 720 nm and 850 nm bandpass filter (middle), and their false color composition (bottom). Photo courtesy of Camilla Perondi.

A color-rendering method resulting from the combination of a visible-color and infrared image by rearranging the respective RGB channels into a derivative image. Traditionally, the Red and Green channels of the visible-color image are assigned to the Green and Blue channels of the new image respectively, while the Green channel of the infrared image is assigned to the Red channel of the new image.

R'=Gir ,  G'=Rvis ,  B'=Gvis

The resulting color rendering may be affected by several factors, among which:

  • Transmittance spectrum of the infrared bandpass filter used for the infrared image
  • Dynamic range and contrast of the infrared image
  • color management performed on the visible image
  • Light falloff or specular reflections across the scene
  • Ageing and discoloration of the object (e.g. yellowing of the finishing varnish).

The nature of the binder in polychrome surfaces doesn't considerably affect the resulting image[2].

If accompanied by a reference chart, the False color Infrared method finds useful application in the identification of pigments, inks and dyes. The chart should include a set of known materials, i.e. pigments, binding media, inks or dyes, applied in a similar fashion as the object under investigation.

False Color Reflected Ultraviolet (FCUV)[edit | edit source]
Reflected UV False Colour of a painting (365 nm)
Visible light photography of a discoloured painting (top), the ultraviolet reflectance image (middle), and their false color composition (bottom). Photo courtesy of Camilla Perondi.

A color-rendering method resulting from the combination of a visible-color and reflected ultraviolet image by rearranging the respective RGB channels into a derivative image. Traditionally, the Green and Blue channels of the visible-color image are assigned to the Red and Green channels of the new image respectively, while the Green channel of the infrared image is assigned to the Blue channel of the new image.

R'=Gvis ,  G'=Bvis ,  B'=Guvr

The resulting color rendering may be affected by several factors, among which:

  • Transmittance spectrum of the UV bandpass filter used for the UV reflectance image
  • Dynamic range and contrast of the UV reflectance image
  • color management performed on the visible image
  • Light falloff or specular reflections across the scene
  • Ageing and discoloration of the object (e.g. yellowing of the finishing varnish).

Neither the nature of the paint binder nor the finishing varnish considerably affect the resulting image.

Chromatic and Infrared Chromatic (CHR and IRCHR)[edit | edit source]

A two-channel based color rendering method used to enhance  the difference in hues and simplify complex color schemes. It finds application in revealing concealed features in dark scenes and in areas of proximate hues, such as dull signatures and other features not visible in the traditional imaging.

Chromatic image of a graffiti
Visible light orthophoto of a graffiti by Blu and Ericailcane (top) and its chromatic image (bottom). Photo courtesy of Camilla Perondi.

From data cube[edit | edit source]

Principal Component Analysis (PCA)[edit | edit source]

A mathematical procedure that uses an orthogonal transformation to convert a data set of variables into a re-ordered set of linearly uncorrelated variables (called principal components) from the same data. The transformation is such that the first principal component has the largest possible variance. PCA essentially rotates the set of data points around their mean in order to align with the principal components and move as much of the variance as possible into the first few dimensions. For example, the page of a book consists of multiple materials including the paper, watermark, underdrawing, inks, pigments, stains, etc.. Each of these elements would be considered a “component”. PCA recognizes the unique spectral signatures of each of these components and provides resulting images that visually highlight each component individually, starting with whichever is most prominent in the image.

Soft Independent Modelling of Class Analogies (SIMCA)[edit | edit source]

A statistical method for classification / pattern recognition of data (based on PCA models). SIMCA defines known pixels into classes and can potentially identify unknowns by fitting them to the defined classes via probability thresholds.

Spectral Angle Mapping (SAM)[edit | edit source]

Spectral classification that matches unknown pixels to known references by determining the similarity between them.

Maximum Intensity Projected (MIP) / Particle Analysis[edit | edit source]

Identifies isolated particles in an image and provides statistics about those particles (how many total, size, etc.).

Workflows and best practices[edit | edit source]

False-color image processing methodologies generally follow “best practice” guidelines like Metamorfoze, FADGI, ISO-19264, CHARISMA[3] and AIC Guide to Digital Photography[4].

When used, it is critical to include both a reading key and a written documentation of the methodology, including the corresponding metadata, for reference and reproducibility.

Processed images should always be analyzed in tandem with the original image to ensure they are not over-manipulated and/or that perceived results are not artifacts.

When processing from an RGB image (e.g. FC IR or FC UV), keep the green channel intact (or take an L* conversion of the visible image and apply to the green channel of the FCI) and replace the red and / or blue channels with the additional (IR / UV) information.

When using more advanced processing techniques like PCA, work from designated regions of interest (ROI) to minimize variables and maximize useful pixels in the data being provided to the algorithm.

Digital preservation standards of using lossless non-proprietary imaging formats (e.g. .TIFF) should be considered throughout processing and especially implemented when saving final images. Processing should be executed with .TIFF file formats, but .PNG derivatives may be used if necessary due to computer or software data size constraints. .JPG should never be used due to pixel compression and introduction of artefacts which severely alter the information.

Workflows[edit | edit source]

Coming soon:

  • Infrared-reflected false-color
  • Ultraviolet-reflected false-color
  • Principal component analysis

How to interpret[edit | edit source]

Examples / Case studies[edit | edit source]

Jill Dunkerton and Marta Melchiorre Di Crescenzo (National Gallery, London) present the results on their research on the painting Adoration of the Kings by Sandro Botticelli and Filippino Lippi.


The following table aims to collect case studies on the use of false color imaging found in scholarly literature:

Field of application Type of object FCI technique Subject(s) Reference
Paintings Mural painting Chromatic Derivative Imaging Mural paints from the Tomb of the Monkey in the Etruscan necropolis of Poggio Renzo Legnaioli et al. 2013[5]
Paintings Easel painting FCIR - Infrared False Color The Virgin’s apparition to Saint Martin, with Saint Agnes and Saint Thecla by Eustache Le Sueur Hayem-Ghez et al. 2015[6]
Paintings Easel painting FCIR - Infrared False Color

PCA - Principal Component Analysis

The Drunkenness of Noah by Andrea Sacchi Pronti et al. 2019[7]
Ceramics Majolica FCIR - Infrared False Color Majolica polychrome decorations Meucci and Carratoni 2016[8]
Paintings Easel painting FCIR - Infrared False Color Adoration of the Kings by Sandro Botticelli and Filippino Lippi Dunkerton et al. 2020[9]


References[edit | edit source]

  1. Moon, Thomas, Michael R. Schilling, and Sally Thirkettle. 1992. ‘A Note on the Use of False-Color Infrared Photography in Conservation’. Studies in Conservation 37 (1): 42.
  2. Cosentino, Antonino. 2015. ‘Effects of Different Binders on Technical Photography and Infrared Reflectography of 54 Historical Pigments’. International Journal of Conservation Science 6 (3): 287–98.
  3. Dyer, Joanne, Giovanni Verri, and John Cupitt. 2013. ‘Multispectral Imaging in Reflectance and Photo-Induced Luminescence Modes: A User Manual
  4. Frey, Franziska S. 2011. The AIC Guide to Digital Photography and Conservation Documentation. Edited by Jeffrey Warda. American Institute for Conservation of Historic and Artistic Works.
  5. Legnaioli, Stefano, Giulia Lorenzetti, Gildo H. Cavalcanti, Emanuela Grifoni, Luciano Marras, Anna Tonazzini, Emanuele Salerno, Pasquino Pallecchi, Gianna Giachi, and Vincenzo Palleschi. 2013. ‘Recovery of Archaeological Wall Paintings Using Novel Multispectral Imaging Approaches’. Heritage Science 1 (1): 33.
  6. Hayem-Ghez, Anita, Elisabeth Ravaud, Clotilde Boust, Gilles Bastian, Michel Menu, and Nancy Brodie-Linder. 2015. ‘Characterizing Pigments with Hyperspectral Imaging Variable False-Color Composites’. Applied Physics A: Materials Science and Processing 121 (3): 939–47.
  7. Pronti, Lucilla, Martina Romani, Gianluca Verona-Rinati, Ombretta Tarquini, Francesco Colao, Marcello Colapietro, Augusto Pifferi, Mariangela Cestelli-Guidi, and Marco Marinelli. 2019. ‘Post-Processing of VIS, NIR, and SWIR Multispectral Images of Paintings. New Discovery on the The Drunkenness of Noah, Painted by Andrea Sacchi, Stored at Palazzo Chigi (Ariccia, Rome)’. Heritage 2 (3): 2275–86
  8. Meucci, Costantino, and Loredana Carratoni. 2016. ‘Identification of the Majolica Polychromatic Decoration by IRFC Methodology’. Journal of Archaeological Science: Reports 8: 224–34
  9. Dunkerton, Jill, Catherine Higgitt, Marta Melchiorre Di Crescenzo, and Rachel Billinge. 2020. ‘A Case of Collaboration: The Adoration of the Kings by Botticelli and Filippino Lippi’. National Gallery Technical Bulletin 41 (2).