This set is the theoretical explanation of the course Digital Image Processing. Here, the common knowledge of the course is given, as what it is about, where it is applicable and how do we get our processed images through examples. Along with that, the processes of digital image processing are explained that will help in the upcoming sets regarding their topics.
At last, the insight to all the topics included in the course are also provided.

Here, we will be looking forward to installing MATLAB and learning how to use it online.

I have provided the link of mathworks website. Along with that, I have provided a PDF containing the steps for creating your mathworks account if case you face any problem.

Also the link is provided as well containing the video made by mathwork to create your account.

In this set we will see how MATLAB as an application works. This is specifically for those who are using MATLAB for the first time as it will be extremely helpful to understand the basics of MATLAB and how variables and matrices are operated in it, which is again very important for Digital Image Processing.

This lecture of the set 3 consists of the Introduction to image processing on MATLAB.

This lecture particularly focuses on how images as matrices are defined, how images are read and displayed, writing images to disk files and how we can see informational contents of images on MATLAB.

This lecture focuses on how images are converted through multiple functions causing different results as outputs on MATLAB.

First we will see how color images can be converted to grayscale images.

Secondly, we will see how color images can be converted to HSV images.

And at last, we'll see how we can convert images / grayscale images into binary images.

This part of the set 3 is particularly small, focusing on the learning of different unit images on MATLAB.
This topic is particularly taught as explore by yourself.

This video is the last of the set 3 Introduction of Digital Image Processing on MATLAB.
This lecture focuses on the sampling and quantization of images hence the process of forming digital images.

This lecture consists of practice questions of set 3 that will help your skills further ahead.

This quiz is related to the basic details of the course we have gone through up until now.

This set is related to many processes, transformations and functions relating to enhancing images on MATLAB.

This lecture of the set 4 consist of the introduction of image enhancement and why do we perform it. Along with that we will be seeing our first process of enhancement that is Point Processing on MATLAB.

This lecture consists of the two important MATLAB functions that fall into the category of image enhancement as they help transform the images on MATLAB.
The first function is 'im2double' MATLAB function, which is used a lot in image enhancement.
The second function is 'mat2gray' MATLAB function.

This lecture is consist of Affine Transformations which help us to translate, scale, rotate and shear images.

This lecture consist of the topic Power Law Transform in Image Enhancement.
They are a class of image enhancement techniques used in digital image processing to adjust the contrast and brightness of an image.

This lecture consist of the topic Contrast-Stretching Transform in Image Enhancement.

Contrast stretching is a simple yet effective method for enhancing the contrast of an image.

This lecture is about how images can be adjusted through multiple ways which helps improving visual quality, highlighting important features, preparing images for analysis, correcting imperfections, and tailoring images for specific applications.

This lecture consists of practice questions of set 4 that will help your skills further ahead.

This set consists of the learning of graphical representation of images on MATLAB and how one can enhance the contrast information in the image with histogram equalization.
Histogram Processing is also a category of Image Enhancement as it helps to convert your image from bad quality to a good quality image that feels like new features are added in your image to enhance it.

From this set, Digital Image Filtering is started and this is the first part of it consisting of the knowledge and study of Smoothing Filters and Noise in images.
In this lecture, the introduction of smoothing filters is discussed with examples and the introduction of noise is explained along with its types with the involvement of MATLAB.

In this lecture, the types of Smoothing Filters are studied using MATLAB.

Smoothing filters are used to reduce noise and smooth out the image. And each type of Smoothing Filter is used to reduce in a different way.

This lecture consists of practice questions of set 6 that will help your skills further ahead.

Quiz 2 is related to section 4, 5 and 6.

In this set, various types of sharpening filters are studied with their respective orders.

In this lecture, first, the introduction of Sharpening Filters will be explained to students that is they enhance the edges and fine details in an image.

Then the Laplacian Filter is discussed and explained with MATLAB.

This lecture consist of the implementation of Highboost Filter also called Unsharp Masking which is used to enhance the edges in images.

This lecture focuses on Sobel and Prewitt filters on MATLAB which are used in image processing to detect edges and boundaries in images.

This lecture consists of practice questions of set 7 that will help your skills further ahead.

In this set, the students will explore the Color Image Processing, focusing on color models of RGB and HSV.

This lecture consist of the basic introduction of color models and then the explanation of RGB color model is discussed where RGB intensities are explored, RGB images are concatenated and the intensity of RGB channels are studied. 

This lecture focuses on the RGB Color Model as well but here we will specifically see how respective red, green and blue colors can be generated on our RGB images.

This lecture is focused on the explanation of HSV Color Models in which the modification of each HSV component is performed to get an enhanced, processed and analyzed image as output.

This lecture consists of practice questions of set 8 that will help your skills further ahead.

This set is focused on the study of Morphological Operations/Processes and its types.

This lecture is about the introduction of Morphological Operations, its processes explained.

Then the concept of Structuring Element which is essential in morphological operations is discussed and studied with MATLAB.

This lecture is focused on the operation of Dilation and Erosion which are primary operations of Morphological Processes.

Dilation is used to expand objects and fill small holes and gaps.

Erosion is used to Shrink objects and removes small noise in binary images.

This lecture is about Opening and Closing Operations of Morphological Processes and extracting the boundary of objects in binary images using Morphological Operations.

Both opening and closing operations are compound operations including dilation and erosion operations.

This lecture is focused on how we can fill images by using multiple ways of Morphological operations.

And then the study of Skeletonizing images is also explored using the Morphological functions.

This lecture consists of practice questions of set 9 that will help your skills further ahead.

Quiz 3 is related to set 7, 8, 9 questions.

This set is about Segmentation Operations and how they are divided into two types called region based and boundary based segmentations.

In this lecture, the students will first go through the introduction of Segmentation Operations and the discussion of its types.

Then thresholding and its respective types are studied which is the type of region-based segmentation.

This lecture is about segmenting a grayscale image by using the threshold method and then adding color on the segmented parts of the image. Hence, this lecture explains the whole process of segmentation.

This lecture is about the boundary/edge based type of segmentation.

Here, we will particularly focus on the point and line detection that are further types of boundary-based segmentation using MATLAB.

This lecture consists of practice questions of set 10 that will help your skills further ahead.