Image Processing: An Introduction

1. Introduction to Image Processing:

According to Rafael C. Gonzalez and Richard E. Woods, “image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image.” Images are everywhere we look. They are in the books we read, the movies we watch, the games we play. We are constantly bombarded with images, and as technology advances, so does the quality and quantity of the images we see. With the advent of digital cameras and smartphones, it has become easy for anyone to capture and share images with others.

2. What is an image?

An image can be defined as a two-dimensional function f(x,y) where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x,y) is called the intensity or gray level of the image at that point. When x,y and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. Most images you will encounter will be digital images. If x,y are continuous variables (like they would be on a graph), then we have what is known as an analog image. In reality, most images are digitized before any further processing takes place (i.e. converted from analog to digital). Images can also be thought of as three-dimensional functions where not only do we have two spatial dimensions (x,y) but also one dimension for intensity (z).

3. Types of images:

There are two main types of images: raster and vector. Raster images are made up of pixels arranged in a grid pattern. Each pixel represents one color in the image, and the tiny squares that make up a raster image are put together so that they appear seamless when viewed from afar. The resolution of a raster image is measured in pixels per inch (ppi), and the higher the resolution, the sharper an image will appear when printed. When it comes to raster images, there are two main types: bitmap images and vector images
Bitmap images, also known as raster images, are made up of pixels arranged in a grid pattern. Each pixel represents one color in the image, and the tiny squares that make up a bitmap image are put together so that they appear seamless when viewed from afar. The resolution of a bitmap image is measured in pixels per inch (ppi), and the higher the resolution, the sharper an image will appear when printed.
Vector images are made up of lines and curves that are mathematically generated by computer software programs such as Adobe Illustrator or CorelDRAW. Vector images can be resized without losing any quality because they are not made up of pixels; instead, they are made up of mathematical formulas that tell your printer how to print each line and curve in your design. When it comes to printing vector images, you will usually see them printed at 300 ppi or higher because this ensures that all of the lines and curves in your design will print cleanly and crisply

4. Components of an Image:

All digital images consist of pixels arranged in rows and columns (or a 2 dimensional grid). The smallest element in this grid is called a Picture Element, commonly known as a pixel. Each pixel has a certain location given by its coordinates (x,y) and each pixel also has a specific color or intensity. When all the pixels are put together, we get the image. The number of pixels in an image is referred to as the resolution of an image

5. Sampling and Quantization:

Sampling is the process of converting a continuous signal (like an image) into a discrete signal (a set of pixels). In order to do this, we need to take samples at regular intervals from the continuous signal. The process of quantization is then applied to these samples in order to assign each sample a numeric value

6. Spatial Resolution:

The spatial resolution of an image is the number of pixels in an image. The higher the spatial resolution, the more detailed an image will be. For example, consider two images of the same scene: one with 1000×1000 pixels, and one with 500×500 pixels. The first image will have twice the spatial resolution of the second image, and will therefore be more detailed.

7. Image Processing using MATLAB:

MATLAB is a powerful tool for image processing because it offers a wide range of built-in functions that can be used to perform various operations on images. In addition, MATLAB also provides a number of toolboxes that extend its functionality even further. Some of these toolboxes are specifically designed for image processing, while others provide functions that can be used for image processing tasks.

8. Functions for Image Processing in MATLAB:

MATLAB provides a number of built-in functions that are useful for image processing tasks. Some of these functions are listed below:
imread() – reads an image from a file
imwrite() – writes an image to a file
imshow() – displays an image in a figure window
imagesc() – displays an image using scaled colors
imresize() – resizes an image
imrotate() – rotates an image
fliplr() – flips an image left-right
flipud() – flips an image top-bottom

9. Reading, Writing and Displaying Images in MATLAB:

The first step in any image processing task is to read an image from a file into MATLAB using the imread() function. This function takes the path to the image file as its input and outputs a matrix that represents theimage. Once we have read an image into MATLAB, we can display it using the imshow() function. This function takes the matrix that represents theimage as its input and displays it in a figure window. If we want to write animage back to a file, we can use the imwrite() function. This function takes the path to the output file and the matrix that represents theimage as its inputs and writes theimage to the specified file. 10. Pre-Processing Images in MATLAB: Pre-processing is any step that is performed on animage before it is fed into some algorithm or function. The purposeof pre-processing is often to improve the quality oftheimage or to make it more suitable for some specific application. In manycases, pre-processing steps are required in orderforan algorithmto work correctly on an image. Some common pre-processing stepsinclude:
Conversion to grayscale
Noise reduction
11. Segmentation of Images in MATLAB: Segmentation is the process of partitioning an image into multiple regions. The simplest way to segment an image is to threshold it such that all pixels with intensities above a certain value are assigned to one region and all pixels with intensities below that value are assigned to another region. This can be done using the threshold() function in MATLAB. More sophisticated methods of image segmentation exist and can be used depending on the application.
12. Representation and Description of Images in MATLAB: After an image has been segmented, each region needs to be represented in some way so that it can be described and analyzed. One common way to represent a region is by its geometric properties, such as its area, perimeter, centroid, etc. Another common way to represent a region is by its intensity properties, such as its mean intensity, variance, etc. These properties can be computed using various functions in MATLAB.
13. Transformations in MATLAB: A transformation is a function that takes an image as input and outputs a transformed image. There are many types of transformations that can be applied to an image, such as rotation, translation, scaling, etc. Transformations can be useful for various purposes, such as improving the quality of an image or making it more suitable for some specific application. In MATLAB, transformations can be performed using the imtranslate(), imrotate(), and imresize() functions.
14. Interface with Other Programming Languages: MATLAB can be used in conjunction with other programming languages, such as C/C++, Fortran, Java, etc., in order to create more complex applications. This is possible because MATLAB provides APIs (Application Programming Interfaces) for these languages. In addition, MATLAB can also be used to call functions written in other languages. For example, if there is a function written in C++ that we want to use in our MATLAB code, we can use the mexFunction() function to call that function from within MATLAB.
15. Conclusion: Image processing is a powerful tool that has many applications in various fields. It allows us to take images as input, perform various operations on them, and output the results of those operations as images. MATLAB is a very popular language for image processing because it offers a wide range of built-in functions and toolboxes that make image processing tasks easier to perform.


MATLAB is a software program that is used for image processing and other numerical computations. It has many capabilities in image processing, including image enhancement, restoration, and compression.

MATLAB can be used to improve the quality of images by removing noise, sharpening edges, and increasing contrast. It can also be used to correct for lens distortion and vignetting.

Some specific examples of how MATLAB has been used to improve images include correcting for exposure errors in photographs, removing blur from images, and improving the quality of scanned documents.

Noise affects image quality by making it more difficult to see details in the image. Noise can be removed usingMATLAB by applying filters or using noise reduction algorithms.

Other benefits that can be achieved throughimage processing with MATLAB include color correction, histogram equalization, and tone mapping.

Restrictions or limitations that should be considered when workingwith images inMATLAB include file format compatibility and memory requirements.

There is a wide variety of Image Processing Toolboxes available for use with MATLAB which offer different functions for image processing tasks such as filtering, edge detection, segmentation, and morphology operations .