Click here to Skip to main content
13,867,908 members
Click here to Skip to main content
Add your own
alternative version

Tagged as


2 bookmarked
Posted 17 Jul 2016
Licenced CPOL

Converting an Image to Grayscale in MatLab

, 17 Jul 2016
Rate this:
Please Sign up or sign in to vote.
Breif tutorial on how to convert an RGB image to grayscale within MatLab


This article is a How To on converting images from RGB to grayscale in MatLab without built-in functions. This will allow you to understand how MatLab works with image layers and know how the underlying RGB to grayscale conversion function works... more importantly, it is always more fun to write functions yourself.


There are many benefits to converting images to grayscale and knowing how to do it. In my opinion, the primary benefit of converting a color image to grayscale is that it takes up considerably less space. If you are new to how images are stored in a computer, let me explain.

An RGB image is essentially three images layered on top of one another; a red scale image, a green scale image, and a blue scale image, with each pixel in them being 8 bits (intensity value ranging 0 - 255). To store a single pixel of an RGB image, you need to store 8 bits for all three colors, so a total of 24 bits per pixel.

When you convert this image to grayscale, you then need to only store a single 8 bit value per pixel for the grayscale value. You go from 24 bits per pixel down to 8 bits per pixel, so your grayscale images will be a mere 33% the size of the originals.

Besides saving you a ton of space, it makes more sense to work with grayscale images in many different situations. Grayscale images are much easier to work with in a variety of image processing applications. Just ask yourself, what would be easier for a computer to operate with, an image containing three layers of color values or an image with a single layer? Pretty clearly, the single layered image. To build on that, it is also easier to distinguish features of an image when only dealing with a single layer. So processes like edge detection, principle component analysis, local binary patterns, and things of that nature are much easier not only for the computer to handle, but for you to code.

Using the Code

If you have never worked with MatLab before, the syntax may be a bit confusing but worry not, it isn't that difficult. Below, you can see the function for converting an RGB image to grayscale.

function returnedImage = toGrayscale(image)
        i = image;
        R = i(:, :, 1);
        G = i(:, :, 2);
        B = i(:, :, 3);
        newImage = zeros(size(i,1), size(i,2), 'uint8');
        for x=1:size(i,1)
           for y=1:size(i,2)
               newImage(x,y) = (R(x,y)*.3)+(G(x,y)*.6)+(B(x,y)*.1);
        returnedImage = newImage;

The first step is to separate the image into three different 2d matrices of R, G, and B. The syntax of "R = i(:, :, 1);" means that you are creating a new matrix R and setting it equal to all the rows and columns of the first layer in image i (hopefully that makes sense). You then create a new image containing all zeros of the same size as the original.

After that, you simply loop through every pixel in the new image, and set that pixel equal to a sum of the R, G, and B pixels in the same location multiplied by a specified weight. As you can see from mine, R is multiplied by .3, G by .6, and B .1. You then return the image.

Your first thought may be to simply take the average of the all three pixels and set that as the value in the grayscale image, but that turns out to be too dark. The weights are really up to you and you can change them as you see fit. For my applications, I found this weighting scheme to work the best.

To implement, you would code something like below:

myImage = imread('faceplantImage.jpg');

title('Original Image');

grayScaleImage = toGrayscale(myImage);
title('Grayscale Image');

From that, you can see your finished grayscale image compared with your original.

Points of Interest

If you are new to MatLab and interested in learning more, here is a great resource:


This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


About the Author

United States United States
Current graduate student at Southern Methodist University in the Computer Science program (Go Mustangs).
Undergraduate degree in Computer Science with a focus on Biometrics from Davenport University (Go Panthers).

You may also be interested in...

Comments and Discussions

QuestionThank you Pin
Rathnakar Shetty Puthige6-Nov-17 4:30
memberRathnakar Shetty Puthige6-Nov-17 4:30 
QuestionNice Pin
Amarnath S17-Jul-16 18:49
professionalAmarnath S17-Jul-16 18:49 
AnswerRe: Nice Pin
dcmuggins18-Jul-16 3:08
professionaldcmuggins18-Jul-16 3:08 
AnswerRe: Nice Pin
Milliarde21-Jul-16 19:00
professionalMilliarde21-Jul-16 19:00 

General General    News News    Suggestion Suggestion    Question Question    Bug Bug    Answer Answer    Joke Joke    Praise Praise    Rant Rant    Admin Admin   

Use Ctrl+Left/Right to switch messages, Ctrl+Up/Down to switch threads, Ctrl+Shift+Left/Right to switch pages.

Permalink | Advertise | Privacy | Cookies | Terms of Use | Mobile
Web02 | 2.8.190214.1 | Last Updated 17 Jul 2016
Article Copyright 2016 by dcmuggins
Everything else Copyright © CodeProject, 1999-2019
Layout: fixed | fluid