Transferring Color To Greyscale Images

Tomihisa Welsh, Michael Ashikhmin, Klaus Mueller


We introduce a general technique for “colorizing” greyscale images by transferring color between a source, color image and a destination, greyscale image.  Although the general problem of  adding chromatic values to a greyscale image has no exact, objective solution, the current approach attempts to provide a method to help minimize the amount of human labor required for this task.  Rather than choosing RGB colors from a palette to color individual components, we transfer the entire color “mood” from the source to the target image by matching luminance and texture information between the images.  We choose to transfer only chromaticity information and retain the original luminance values of the target image.  The procedure is further enhanced by allowing the user to match areas of the two images with rectangular swatches.  We show that this simple technique can be successfully applied to a variety of images and video provided that texture and luminance are sufficiently distinct.  The images we have generated demonstrate the potential and utility of our technique in a diverse set of application domains.

 Full Paper to appear in SIGGRAPH 2002 (3.9 Mb)


Our concept of transferring color from one image to another is inspired by work by Reinhard et al. [CG&A Sept/Oct 2001] in which color is transferred between two color images. In their work, colors from a source image are transferred to a second colored image using a simple but surprisingly successful procedure. Since both the source and target spaces have color,  matching involves 3 channels.  However, greyscale images only contain one color channel (luminance), so we can only match values between the luminance channel of the source and target images.  We use neighborhood statistics to help guide the matching process.  Once the best match is found, we transfer only color value for that pixel and retain the original luminance value of the target pixel.  In difficult cases, a few swatches can be used to aid the matching process between the source and the target image. After color is transferred between the source and the target swatches, the final colors are assigned to each pixel in the greyscale image by matching each greyscale image pixel to a pixel in the target swatches using the L2 distance metric. Thus, each pixel match is determined by matching it only to other pixels within the same image.  Figure 2 shows the basic idea.
Figure 2: The two variations of the algorithm. (a) Source color image. (b) Result of basic, global algorithm applied (no swatches). (c) Greyscale image with swatch colors transferred from Figure 2a. (d) Result using swatches.


Figure 3:


Please Note:
Figure 1: Source courtesy (c) Ian Britton -
Figure 3a: Images courtesy of Adam Superchi and Philip Greenspun.
Figure 3c: The Ansel Adams photograph was orginally commissioned by the Department of the Interior.  The source image is courtesy of Paul Kienitz.
Figure 3f: The SEM photograph is courtesy of Scott Chumbley.


For all  three movie clips, we used swatches to colorize a single frame in the movie sequence.  Then the colorized target swatch for the frame was used as the source samples for all other frames in the movie sequence.  Note, the original target movie clips were color but turned to greyscale for demonstration purposes.


 View MPEG Movie

Video courtesy of National Geographic.


View MPEG video clip

Video courtesy of National Geographic.

Brain Volume

This brain volume was originally from the Visible Human dataset.  The movie was obtained at

View MPEG video clip