Image Processing Toolbox

Saurabh Singh
Sep 6, 2018 · 2 min read

Image Processing Toolbox provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.

Image Processing Toolbox apps let you automate common image processing workflows. The Image Processing toolbox is a collection of functions that extend the capabilities of the MATLAB’s numeric computing environment. The toolbox supports a wide range of image processing operations, including:

Geometric operations

Neighborhood and block operations

Linear filtering and filter design

Transforms

Image analysis and enhancement

Binary image operations

Region of interest operations

Image processing is the technique to convert an image into digital format and perform operations on it to get an enhanced image or extract some useful information from it. Changes that take place in images are usually performed automatically and rely on carefully designed algorithms.

Image processing is a multidisciplinary field, with contributions from different branches of science including mathematics, physics, optical and electrical engineering. Moreover, it overlaps with other areas such as pattern recognition, machine learning, artificial intelligence and human vision research. Different steps involved in image processing include importing the image with an optical scanner or from a digital camera, analyzing and manipulating the image (data compression, image enhancement and filtering), and generating the desired output image.

The need to extract the information from images and interpret their content has been the driving factor in the development of image processing. Image processing finds use in numerous sectors, including medicine, industry, military,consumer electronics and so on.

In medicine, it is used for diagnostic imaging modalities such as digital radiography, positron emission tomography(PET), computerized axial tomography(CAT), magnetic resonance imaging(MRI) and functional magnetic resonance imaging(FMRI). Industrial applications include manufacturing systems such as safety systems, quality control and automated guided vehicle control.

Display Functions-

image- create and display image object

imagesc- scale and display as image

imshow- Display image

colorbar- display colorbar

getimage- get image data from axes

truesize- adjust display size of image

zoom- zoom in and zoom out of 2D plot

Image Conversion Functions-

gray2ind- intensity image to index image

im2bw- image to binary

im2double- image to double precision

im2uint8- image to 8 bit unsigned integers

im2uint16- image to 16 bit unsigned image

ind2gray- indexed image to intensity image

mat2gray- matrix to intensity image

rgb2gray- RGB image to greyscale

rgb2ind- RGB image to indexed image

imhist- Display a histogram of image data

imadjust- Adjusts image intensity values.

Filter Functions-

Mean Filter- mf=ones(3,3)/9

Data Filter- Y=filter2(h,X) filters the data in X with the 2D FIR filter in the matrix h.

Median Filter-

I = imread(‘cameraman.tif’);
J = imnoise(I,’salt & pepper’,0.02);
K = medfilt2(J);
subplot(121);imshow(J);
subplot(122);imshow(K);