ind2rgb

Convert indexed image to RGB image

Syntax

RGB = ind2rgb(X,map)

Description

RGB = ind2rgb(X,map) converts the indexed image X and corresponding colormap map to RGB (truecolor) format.

Examples

Convert Indexed Image to RGB

Read the first image in the sample indexed image file, corn.tif.

[X,cmap] = imread('corn.tif');

The indexed image X is a 415-by-312 array of type uint8. The colormap cmap is a 256-by-3 matrix of type double, therefore there are 256 colors in the indexed image. Display the image.

imshow(X,cmap)

Convert the indexed image to an RGB image. The result is a 415-by-312-by-3 array of type double.

RGB = ind2rgb(X,cmap);

Check that values of the RGB image are in the range [0, 1].

disp(['Range of RGB image is [',num2str(min(RGB(:))),', ',num2str(max(RGB(:))),'].'])

Range of RGB image is [0.0078431, 0.97647].

Input Arguments

X — Indexed image

m-by-n matrix of integers

Indexed image, specified as an m-by-n matrix of integers.

• If you specify X as an array of integer data type, then the value 0 corresponds to the first color in the colormap map. For a colormap containing c colors, values of image X are clipped to the range [0, c-1].

• If you specify X as an array of data type single or double, then the value 1 corresponds to the first color in the colormap. For a colormap containing c colors, values of image X are clipped to the range [1, c].

Data Types: single | double | uint8 | uint16

map — Colormap

c-by-3 matrix

Colormap associated with indexed image X, specified as a c-by-3 matrix with values in the range [0, 1]. Each row of map is a three-element RGB triplet that specifies the red, green, and blue components of a single color of the colormap.

Data Types: double

Output Arguments

RGB — RGB image

m-by-n-by-3 numeric array

RGB image, returned as an m-by-n-by-3 numeric array with values in the range [0, 1].

Data Types: double

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a