An implementation of the
  Enhanced Image Colour Transfer
  methods developed by T. E. Johnson,
  with an additional choice of
  lαβ and CIE L*a*b colour space.

  Scroll down for further information.


         
 
Target Image
Palette Image
  





  




  
       
  



Processing Guidance:

-Introductory Exercise:
  - From the 'Samples' drop-down menu select the 'Leaning Tower' images.
  - Click the 'Generate output image' button.
  - Click on the 'Output' image to download it.

 This exercise demonstrates the principles of processing. The output image is a version of the 'Target'
 image that has been re-coloured to match the shading of the 'Palette' image.

-Image Selection
  Users may select their own images for processing by clicking upon the 'Target' and 'Palette' image panels
  respectively, and navigating to the directory where the images are located. Additionally, images may be
  dragged and dropped onto the respective images panels.

-Processing Options
  Processing parameters are initially set to recommended default values but these can be varied
  by adjustment of the slider bars. The default processing is based upon the lαβ colour space but
  the CIE L*a*b colour space may be selected by means of a drop-down menu. Further details on
  the setting of processing parameters can be found here. Details of the processing methods
  can be found here. Once the processing parameters have been adjusted as required, then
  click the 'Generate output image' button.

Web App Features:                                                                             (M. Renzullo)
  - Choice of CIE L*a*b* and l-alpha-beta colour space processing
  - Potential for simple upgrade to other colour spaces.
  - User image selection by drag-and-drop or by click and navigation.
  - Automatic output image download by click-on-canvas
  - Worker Thread
  - Very light 650 KiB achieved by utilisation of early software version.
  - (Built with minimal OpenCV 2.4.10, only 'core' and 'imgproc' modules)

General:
  This web app has been designed to operate on a desktop computer.

  On some mobile devices, large images may require more RAM resource
  to expedite processing than the browser permits.

  Please show your appreciation by adding a star to our GitHub repositories.

Processing Guidance:

-Introductory Exercise:
  - From the 'Samples' drop-down menu
     select the 'Leaning Tower' images.
  - Click the 'Generate output image'
     button.
  - Click on the 'Output' image to
     download it.

  This exercise demonstrates the
  principles of processing. The output
  image is a version of the 'Target' image
  that has been re-coloured to match the
  shading of the 'Palette' image.

-Image Selection
  Users may select their own images for
  processing by clicking upon the 'Target'
  and 'Palette' image panels respectively,
  and navigating to the directory where
  the images are located. Additionally,
  images may be dragged and dropped
  onto the respective images panels.

-Processing Options
  Processing parameters are initially set to
  recommended default values but these
  can be varied by adjustment of the
  slider bars. The default processing
  is based upon the lαβ colour space
  but the CIE L*a*b colour space may
  may be selected by means of a
  drop-down menu. Further details on
  the setting of processing parameters
  can be found here. Details of the
  processing methods can be found here.
  Once the processing parameters have
  been adjusted as required, then
  click the 'Generate output image'
  button.

Web App Features:                       (M. Renzullo)
  - Choice of CIE L*a*b* and l-alpha-beta
     colour space processing
  - Potential for simple upgrade to other
     colour spaces.
  - User image selection by drag-and-
     drop or by click and navigation.
  - Automatic output image download by
     click-on-canvas
  - Worker Thread
  - Very light 650 KiB achieved by
     utilisation of early software version.
  - (Built with minimal OpenCV 2.4.10,
     only 'core' and 'imgproc' modules)

General:
  This web app has been designed to
  operate on a desktop computer.

  On some mobile devices, large images
  may require more RAM resource to
  expedite processing than the
  browser permits.

  Please show your appreciation by
  adding a star to our GitHub repositories.