Digital Image Processing Course And Certification

What is Digital Image Processing?

Digital Image Processing is the use of computing algorithms to perform image processing on digital images to improve image quality, extract meaningful information or mapping and recognition of objects.

Digital Image is an image or picture rendered digitally i.e., in groups of sequences bits or respectively called pixels. Digital Image Processing deals with manipulating these groups of bits (or pixels) to enhance the quality of the image or create different perspectives or to extract information from the image digitally, with the help of computer algorithms.

The Use of Digital Imaging has been increasing exponentially in the last decades. Its applications range from medicine to entertainment, passing by geological processing and remote sensing, even multimedia systems one of the pillars of the modern information society rely heavily on digital image processing.

What is an Image?

An image can be defined by a two-dimensional array specifically arranged in rows and columns. It is nothing more than a two-dimensional signal.

Any Image is a two-dimensional function, F(x,y), where x and y are spatial coordinates, and the amplitude of F at any pair of coordinates (x,y) is called the intensity of that image at that point. The value of f(x,y) at any point given is the pixel value at that point of an image.

Image is composed of a finite number of elements, each of which elements have a particular value at a particular location.

Types of Images: 

Binary Image: A binary image has only two possible gray values or intensities 0 and 255, there are no intermediate values. Binary images are used as masks for indicating the pixels of interest in many image processing tasks. This has only two-pixel elements i.e 0 & 1, where 0 refers to black and 1 refers to white. This is also known as Monochrome.

Grayscale Image: Grayscale image has a range of values from 0 to 255 i.e, each pixel location can have any value between 0 and 255. Old films around the 1950s are actually grayscale images. The image which consists of only black and white color is also called black and white image.

Color Image: Both binary image and grayscale image are 2-dimensional arrays, where at every location, you have one value to represent the pixel. We need more than one value for each pixel to represent a color. Typically you need 3 values for each pixel to represent any color. This has 65,536 different colors in it. It is also known as the High Color Format.

Digital Image Processing Phases: 

Digital Image Processing consists of various phases which are:

Image Acquisition: It could be as simple as being given an image that is in digital form. The main work involves:

a) Scaling.

b) Color conversion(RGB to Gray or vice-versa).

Image Enhancement: It is the simplest and most appealing in areas of Image Processing. It is used to increase and enhance the quality of the image. It is also used to extract some hidden details from an image and is subjective.

Image Restoration: It deals with the restoration of an image but it is objective. It is based on a mathematical or probabilistic model or image degradation.

Color Image Processing: It deals with pseudocolor and full-color image processing color models that are applicable to digital image processing.

Wavelets and Multi-Resolution Processing: It is the foundation of representing images in various degrees.

Image Compression: It involves developing some functions to perform this operation. It mainly deals with image size or resolution.

Morphological Processing: It deals with tools for extracting image components that are useful in the representation & description of shape.

Segmentation Procedure: It includes partitioning an image into its constituent parts or objects. Autonomous segmentation is the most difficult task in Image Processing.

Representation and Description: It follows the output of the segmentation stage, choosing a representation is only the part of the solution for transforming raw data into processed data.

Object Detection and Recognition: It is a process that assigns a label to an object based on its descriptor.

Advantages of Digital Image Processing

1. Easy manipulation of Images

2. Enhance the quality of an image

3. You can save a file digitally and it can be uploaded in a matter of seconds.

4. Extract meaningful insights from images

5. Object detection and recognition 

6. Easy and fast image optimization

7. Compact storage.

Digital Image Processing Course Outline

Digital Image Processing - Introduction

Digital Image Processing - Signal and System Introduction

Digital Image Processing - History of Photography

Digital Image Processing - Applications and Usage

Digital Image Processing - Concept of Dimensions

Digital Image Processing - Image Formation on Camera

Digital Image Processing - Camera Mechanism

Digital Image Processing - Concept of Pixel

Digital Image Processing - Perspective Transformation

Digital Image Processing - Concept of Bits Per Pixel

Digital Image Processing - Types of Images

Digital Image Processing - Color Codes Conversion

Digital Image Processing - Grayscale to RGB Conversion

Digital Image Processing - Concept of Sampling

Digital Image Processing - Pixel Resolution

Digital Image Processing - Concept of Zooming

Digital Image Processing - Zooming methods

Digital Image Processing - Spatial Resolution

Digital Image Processing - Pixels Dots and Lines per inch

Digital Image Processing - Gray Level Resolution

Digital Image Processing - Concept of Quantization

Digital Image Processing - ISO Preference curves

Digital Image Processing - Concept of Dithering

Digital Image Processing - Histograms Introduction

Digital Image Processing - Brightness and Contrast

Digital Image Processing - Image Transformations

Digital Image Processing - Histogram Sliding

Digital Image Processing - Histogram Stretching

Digital Image Processing - Introduction to Probability

Digital Image Processing - Histogram Equalization

Digital Image Processing - Gray Level Transformations

Digital Image Processing - Concept of convolution

Digital Image Processing - Concept of Masks

Digital Image Processing - Concept of Blurring

Digital Image Processing - Concept of Edge Detection

Digital Image Processing - Prewitt Operator

Digital Image Processing - Sobel operator

Digital Image Processing - Robinson Compass Mask

Digital Image Processing - Krisch Compass Mask

Digital Image Processing - Laplacian Operator

Digital Image Processing - Frequency Domain Analysis

Digital Image Processing - Fourier series and Transform

Digital Image Processing - Convolution theorem

Digital Image Processing - High Pass vs Low Pass Filters

Digital Image Processing - Introduction to Color Spaces

Digital Image Processing - JPEG compression

Digital Image Processing - Optical Character Recognition

Digital Image Processing - Computer Vision and Graphics

Digital Image Processing - Video Lectures 

Digital Image Processing - Exams and Certification