Colour to grey scale and related transforms
Lead Research Organisation:
University of East Anglia
Department Name: Computing Sciences
Abstract
Every year, millions of poor grey scale reproductions of colour images are made. Through our research we aim to make millions more good grey scale prints.It is now increasingly common to capture images using colour digital cameras and display them on colour monitors or using colour inkjet printers. However, there are still many occasions when colour images are reproduced in greyscale. The use of black and white printers, photocopiers and fax machines is still an every day occurrence. Unfortunately, the conversion of colour images to their greyscale equivalent, either by these devices, or through other means, often results in a poor reproduction of the images. The aim of our research is to develop a method to derive the best possible greyscale reproduction of a colour image, by a careful consideration of the limitations of our own visual system, and by exploiting the underlying physics of colour image formation. Usually, the colour at a given image point is coded by three numbers, and in the greyscale transformation these three numbers are reduced to just one grey value. More generally, colour information might be coded using N (where N>3) numbers, and in this case it is useful to be able to derive methods to reduce these N numbers to 3, or fewer, so that the N-dimensional information recorded can be displayed on conventional imaging technology. We will extend our research to also consider such cases. Our research will lead to the improved reproduction of images on greyscale devices (e.g. improved photocopying and faxing) as well as providing us with the ability to better visualise the information contained in, for example, satellite images. We also expect that it will be possible to exploit the technology we develop to make colour images that can be viewed without error or confusion, by colour-blind observers. In addition, the work will lead to improvements in existing image processing algorithms, and to a better understanding of how our own visual system perceives colour and brightness information.
People |
ORCID iD |
Graham Finlayson (Principal Investigator) |
Publications

Bloj M
(2010)
Preferred greyscale versions of coloured images: Human vs machine
in Journal of Vision

Bloj M
(2009)
Consistent grey-level ordering for iso-luminant and iso-saturated colours,
in Perception

Bloj M
(2010)
What do we know about how humans choose grey levels for images?
in Journal of Vision

Connah D
(2012)
Human Assignment of Grey Levels Depends on Scene Complexity


Connah D
(2008)
A novel approach to hue ordering


Connah D
(2008)
An investigation into perceptual hue-ordering
Description | Every year, millions of poor grey scale reproductions of colour images are made. Through our research we aim to make millions more good grey scale prints. It is now increasingly common to capture images using colour digital cameras and display them on colour monitors or using colour inkjet printers. However, there are still many occasions when colour images are reproduced in greyscale. The use of black and white printers, photocopiers and fax machines is still an every day occurrence. Unfortunately, the conversion of colour images to their greyscale equivalent, either by these devices, or through other means, often results in a poor reproduction of the images. The aim of our research is to develop a method to derive the best possible greyscale reproduction of a colour image, by a careful consideration of the limitations of our own visual system, and by exploiting the underlying physics of colour image formation. Usually, the colour at a given image point is coded by three numbers, and in the greyscale transformation these three numbers are reduced to just one grey value. More generally, colour information might be coded using N (where N>3) numbers, and in this case it is useful to be able to derive methods to reduce these N numbers to 3, or fewer, so that the N-dimensional information recorded can be displayed on conventional imaging technology. We will extend our research to also consider such cases. Our research will lead to the improved reproduction of images on greyscale devices (e.g. improved photocopying and faxing) as well as providing us with the ability to better visualise the information contained in, for example, satellite images. We also expect that it will be possible to exploit the technology we develop to make colour images that can be viewed without error or confusion, by colour-blind observers. In addition, the work will lead to improvements in existing image processing algorithms, and to a better understanding of how our own visual system perceives colour and brightness information. |
Description | This grant led to the EP/IO28455 of (follow up funding) which began the commercialization of the technology developed in this grant. Briefly, our research developed methods for fusing multichannel images to make a grey-scale or colour image which naturally and without artifact portrayed all the information from all the bands. Second our research was designed to make best use of our own visual system. Image fusion for photograph (e.g. RGB+Near Infra Red) and for visual accessibility (helping colour deficient observers see like we do) is being commercialised by Spectral Edge Ltd (spun out of Finlayson's lab) Below, many sectors are 'ticked' as the spectral edge approach is a platform technology. Ticked are all the areas where prototype applications have been demonstrated |
First Year Of Impact | 2012 |
Sector | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Education,Environment,Healthcare,Manufacturing, including Industrial Biotechology,Security and Diplomacy |
Impact Types | Economic |
Description | SpectralEdge Image Visualisation |
Amount | £63,742 (GBP) |
Funding ID | EP/I028455/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 12/2010 |
End | 08/2011 |
Description | Xerox Corporation |
Organisation | Xerox Corporation |
Country | United States |
Sector | Private |
Start Year | 2006 |
Title | IMAGE RECONSTRUCTION METHOD AND SYSTEM |
Description | A method and system for producing a scalar image from a derivative field and a vector image is disclosed. A function class c is selected, where all members of the class c are functions which map each vector of the vector image to a unique scalar value. A function f is selected from the class c which maps the vector image to a scalar image, the derivative of which is closest to the derivative field. The scalar image is generated from the vector image by using f to calculate each scalar value in the scalar image from a corresponding vector in the vector image. |
IP Reference | WO2011021012 |
Protection | Patent application published |
Year Protection Granted | 2011 |
Licensed | Yes |
Impact | This patent is a key foundation of Spectral Edge's - a UEA spin out - business. It subserves its work on image fusion and image enhancement. Spectral Edge was acquired by an industry major at the end of 2019 |
Title | METHOD AND SYSTEM FOR GENERATING ACCENTED IMAGE DATA |
Description | A method and system for producing accented image data for an accented image is disclosed. The method includes decomposing each of a first and a second image into a gradient representation which comprises spectral and edge components. The first image comprises more spectral dimensions than the second image. The edge component from the first image is combined with the spectral component from the second image to form a combined gradient representation. Accented image data for the accented image is then generated from data including the combined gradient representation. |
IP Reference | US2011052029 |
Protection | Patent granted |
Year Protection Granted | 2011 |
Licensed | Yes |
Impact | Spectral Edge Ltd, a spin out from the university of East Anglia, is taking this technology to market. The company is focused on developing technology to fuse RGB+Near Infrared (or thermal) images for the photographic and surveillance industries. Spectral Edge was acquired by an industry major at the end of 2019 |