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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • On-demand video*
  • E-Book
  • Self-Paced Learning
  • Certificate of Completion

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Digital Image Processing Course covers the core concepts of image processing. The digital Image processing term refers to dealing with images that are available digitally and ready to process, including editing and retouching that image. This is the course that includes all the basic know-how of Digital image processing online course work. In this course, you will learn how to enhance the quality of the image to make it more attractive. Also, you will be explained how to automate for quality control of the image. You will also explore the wide range of digital image processing techniques, algorithms, and many other applications to use.

Why should you enroll in this course?

  • You will have an effective understanding of image processing concepts.
  • This course will cover information about pixels, camera models, and imaging geometry.
  • There are dedicated modules for image enhancement, transformation, restoration, and processing of color images.
  • You will learn other essential topics as well, with practical examples.

So, if you enroll in the course, then you will have a fundamental knowledge of image processing with vital mathematical frameworks with dimensional information. This course is specially designed to make students aware of digital image processing to make images more attractive and professional. Moreover, if you are passionate about getting new skills with the latest updates, then this is the course you should get. This will give you vital information about processing images digitally within a given framework. To take this online course, you need to be created a simple registration form and need to buy a course. After completion of the course you shall add image processing skills to your CV and get a chance to work with companies.

What is digital image processing?

Using digital algorithms to process digital images with the help of a digital computer is known as digital image processing. In simpler words, image processing is performing different operations upon images to enhance their quality. It is a set of processes under which the input is the image that you want to process to extract the information. And, the output can be the features or characteristics that you wished to have developed. One of the rapidly growing technologies, digital image processing is gathering the interest of many students. Especially among the computer science and engineering graduates.

What steps do digital image processing applications include?

Image processing comprises three steps. Let's know these three steps-

  • Importing/Uploading the image using Image acquisition tools.
  • First analyzing and then manipulating the image
  • Receiving the output under which you can have the altered image or the extracted information. Whatever you should get.

What factors affect the generation and development of digital image processing?

Three factors affect the generation and development of digital image processing. Let's know them-

  • The development of digital computers
  • The developments in mathematics, and,
  • The high demand for applications in different economic and social sectors. Such as agriculture, environment, military, and medical sciences.

What is an image?

A two-dimensional surface captured by an optical device such as a camera is an image. A digital image comprises rows and columns specifically arranged in a two-dimensional array. Moreover, a digital image comprises finite elements. And each one of these elements has a particular value stored at a particular place. These elements are pixels, picture elements, and image elements. But, Pixels are the most common term used to denote the elements of a digital image. There are 4 different types of images. Let's know these types in detail.

Binary Image

Also known as Monochrome, Binary Image comprises two-pixel elements only. These elements are 0 and 1. Here 0 is referring to black, and 1 refers to white.

Black & White Image

An Image made up of only two colors, black and white, is a black & white image.

8 Bit Color Format Image

A 256 colored image is an 8-Bit color format image. Also known as the grey-scale image, an 8-Bit color image is the most popular among all. Provided that 0 is for black, 127 is for grey, and 255 is for white.

16 Bit Color Format Image

With the most colors, the 16-Bit color format image offers more colors than the gray-scale format. Besides 65,536 colors, the 16-Bit color format image distributes colors differently than the popular 8-Bit color format. Also known as High Color Format, the 16-Bit Color Format is divided into three further formats. And, these formats are the Red, Green, and Blue formats. Thus known as the RGB Format.

What are Image Sensors?

An image sensor is an electronic device. Which converts optical images into electronic signals. Thus, used in digital cameras to reflecting the light falling upon the camera into the digital image.

What is Image Compression?

Simply saying, Image compression is reducing the byte size of images without reducing their quality. Helping in storing more digital images in less space. Also, it helps in reducing the time that an image takes to be uploaded over the internet. Or to be downloaded from it.

What are the different phases of digital image processing?

Digital image processing is a set of ten different phases. And, only after going through these 10 different phases, you can conclude an image as a digitally processed image. Let's know the 10 different phases of digital image processing-

Image Acquiring

The process includes retrieving an image from a source. Here it may or may not be a hardware-based source. The first step includes the acquisition of an image. As without it, no further processing can be done. The image obtained is completely raw. Basically, under acquisition, scaling, and color conversion (Gray to RGB and vice versa) of an image is performed.

Image Enhancing

It includes making adjustments to digital images. Thus, the results become more suitable for displaying or analyzing purposes. Sharpening the image, brightening it, noise removal, and contrast adjustments are all parts of this phase. While enhancing an image, we modify the image to make it look more eye-pleasing.

Image Restoring

Recovering the degraded images because of different conditions is restoring images. The process is different from image enhancement and is completely objective.

Image Color Processing

The image color processing includes the processing of either RGB images or indexed images.

Multi-resolution and Wavelets Processing

Wavelets provide time-frequency information using small waves of limited durations. Which leads to multi-resolution processing. It includes the representation of images through various degrees of resolution.

Image Compressing

Compression is a unique technique that reduces the byte size of images without reducing their quality. It helps in saving some extra space on your hard disks and drives. Also, an image that is less or compressed is easy to upload and download. And takes very little time in the process than the actual image. Thus it is used to display images over the internet. A compressed image requires less amount of data. Making it easier to display an image from a server. Thus increasing the loading speed of the website.

Morphological Image Processing

It includes the extraction of components of an image. Helping in its representation and description of shape. Operations like dilation and erosion are parts of morphological image processing. And the output of it is image attributes.

Image Segmenting

Dividing a digital image into different segments is image segmenting. Its primary uses are locating images and developing image boundaries.

Image Representation and Description

Basically, under Image representation and description, we convert the image data into a suitable computer processing form. As it includes boundary representation and regional representation.

Boundary Representation

Boundary Representation focuses upon the external shape characteristics of an image. Thus, it includes borders and corners.

Regional Representation

Regional representation is the opposite of Boundary Representation. Because it focuses upon the internal characteristics of an image. Including the texture or quality of the image. It is also used as a differentiator of two classes.

Image Recognizing

It includes the last and the labeling phase of an image. Thus, naming the digital processed image comprises Image Recognition.

There is also a course for you if you want to enhance your knowledge or looking for certification in digital signal processing

Course Content

Total: 123 lectures
  • What is an image?
  • Resolution and quantization
  • Image formats
  • Colour spaces
  • Images in Matlab
  • How is an image formed?
  • The mathematics of image formation
  • The engineering of image formation
  • What is a pixel?
  • Operations upon pixels
  • Point-based operations on images
  • Pixel distributions: histograms
  • Why perform enhancement?
  • Pixel neighbourhoods
  • Filter kernels and the mechanics of linear filtering
  • Filtering for noise removal
  • Filtering for edge detection
  • Edge enhancement
  • Frequency space: a friendly introduction
  • Frequency space: the fundamental idea
  • Calculation of the Fourier spectrum
  • Complex Fourier series
  • The 1-D Fourier transform
  • The inverse Fourier transform and reciprocity
  • The 2-D Fourier transform
  • Understanding the Fourier transform: frequency-space filtering
  • Linear systems and Fourier transforms
  • The convolution theorem
  • The optical transfer function
  • Digital Fourier transforms: the discrete fast Fourier transform
  • Sampled data: the discrete Fourier transform
  • The centred discrete Fourier transform
  • Imaging models
  • Nature of the point-spread function and noise
  • Restoration by the inverse Fourier filter
  • The Wiener–Helstrom Filter
  • Origin of the Wiener–Helstrom filter
  • Acceptable solutions to the imaging equation
  • Constrained deconvolution
  • Estimating an unknown point-spread function or optical transfer function
  • Blind deconvolution
  • Iterative deconvolution and the Lucy–Richardson algorithm
  • Matrix formulation of image restoration
  • The standard least-squares solution
  • Constrained least-squares restoration
  • Stochastic input distributions and Bayesian estimators
  • The generalized Gauss–Markov estimator
  • The description of shape
  • Shape-preserving transformations
  • Shape transformation and homogeneous coordinates
  • The general 2-D affine transformation
  • Affine transformation in homogeneous coordinates
  • The Procrustes transformation
  • Procrustes alignment
  • The projective transform
  • Nonlinear transformations
  • Warping: the spatial transformation of an image
  • Overdetermined spatial transformations
  • The piecewise warp
  • The piecewise affine warp
  • Warping: forward and reverse mapping
  • Introduction
  • Binary images: foreground, background and connectedness
  • Structuring elements and neighbourhoods
  • Dilation and erosion
  • Dilation, erosion and structuring elements within Matlab
  • Structuring element decomposition and Matlab
  • Effects and uses of erosion and dilation
  • Morphological opening and closing
  • Boundary extraction
  • Extracting connected components
  • Region filling
  • The hit-or-miss transformation
  • Relaxing constraints in hit-or-miss: ‘don’t care’ pixels
  • Skeletonization
  • Opening by reconstruction
  • Grey-scale erosion and dilation
  • Grey-scale structuring elements: general case
  • Grey-scale erosion and dilation with flat structuring elements
  • Grey-scale opening and closing
  • The top-hat transformation
  • Landmarks and shape vectors
  • Single-parameter shape descriptors
  • Signatures and the radial Fourier expansion
  • Statistical moments as region descriptors
  • Texture features based on statistical measures
  • Principal component analysis
  • Principal component analysis: an illustrative example
  • Theory of principal component analysis: version 1
  • Theory of principal component analysis: version 2
  • Principal axes and principal components
  • Summary of properties of principal component analysis
  • Dimensionality reduction: the purpose of principal component analysis
  • Principal components analysis on an ensemble of digital images
  • Representation of out-of-sample examples using principal
  • component analysis
  • Key example: eigenfaces and the human face
  • Image segmentation
  • Use of image properties and features in segmentation
  • Intensity thresholding
  • Region growing and region splitting
  • Split-and-merge algorithm
  • The challenge of edge detection
  • The Laplacian of Gaussian and difference of Gaussians filters
  • The Canny edge detector
  • Interest operators
  • Watershed segmentation
  • Segmentation functions
  • Image segmentation with Markov random fields
  • The purpose of automated classification
  • Supervised and unsupervised classification
  • Classification: a simple example
  • Design of classification systems
  • Simple classifiers: prototypes and minimum distance criteria
  • Linear discriminant functions
  • Linear discriminant functions in N dimensions
  • Extension of the minimum distance classifier and the Mahalanobis distance
  • Bayesian classification: definitions
  • The Bayes decision rule
  • The multivariate normal density
  • Bayesian classifiers for multivariate normal distributions
  • Ensemble classifiers
  • Unsupervised learning: k-means clustering


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