INTRODUCTION TO DIGITAL IMAGE PROCESSING.pptx

Introduction to Digital Image Processing

1. DIGITAL IMAGE PROCESSING Dr. GOWTHAMI V

2. Digital image processing Digital image processing  is a critical field in modern technology, involving the conversion and manipulation of visual information for various applications.

3. Image An image is a digital representation of a visual scene, captured and stored in an electronic format. Essentially, it's an array of pixels, each holding specific information about colour and intensity. The arrangement and composition of these pixels determine the overall appearance and quality of the image. From a technical standpoint, images can be categorized into various types, such as grayscale, colour , binary, and more, each requiring a unique approach to processing.

4. Image Processing Image processing , a pivotal element in the fundamental steps of digital image processing, involves the manipulation and analysis of digital images using  computational techniques . This process aims to enhance image quality, extract valuable information, or transform images for better interpretation. It encompasses a broad range of operations, like resizing and colour adjustments to complex algorithms for pattern recognition and feature extraction. Image processing is not just about altering the visual aesthetics but also about enabling efficient storage, transmission, and accurate analysis of digital images.

5. The techniques used in image processing are categorized into  two main types:   analogue and digital . While analogue processing deals with physical prints and films , digital image processing focuses on the computational manipulation of digital images .

6. Types of Image Binary Images:  These are the simplest type of images, consisting of only two colours , typically black and white . Each pixel in a binary image can have one of two possible values, representing distinct features or objects in the image. Binary images are often used in applications where the focus is on the shape or outline of objects, such as text recognition and geometric pattern analysis .

7. Grayscale Images:  Grayscale images contain shades of grey , providing more detail than binary images. Each pixel in a grayscale image typically carries a value representing the intensity of light at that point, with varying levels of brightness from black to white . Grayscale images are widely used in applications like medical imaging, where detail and contrast are important but colour  information isn't necessary .

8. Color Images:  These images are the most complex and contain coloured pixels, usually represented in the RGB (Red, Green, Blue) colour model. Each pixel in a colour image has three values corresponding to the red, green, and blue colour components, allowing for a wide range of colours . Colour images are crucial in fields where visual appearance and colour information are vital, like in digital photography and video processing .

9. Indexed Images:  In indexed images, each pixel value points to a colour in a separate colour palette or lookup table . This type of image is efficient for storing graphical images like icons and maps , where a limited colour palette suffices.

10. Types of Image Processing Low-Level Processing:  This type involves primitive operations such as  noise reduction, contrast enhancement, and image sharpening . The aim is to improve the image quality without altering the underlying content. Low-level processing is often the first step in the fundamental steps of digital image processing, preparing the image for further analysis or manipulation .

11. Mid-Level Processing:  Mid-level processing involves tasks like  segmentation , object recognition, and feature extraction . This type of processing interprets data in the image and is crucial for applications requiring the identification and analysis of various objects within an image .

12. High-Level Processing:  High-level processing involves ' understanding ' the contents of an image , often mimicking human visual perception. It includes complex operations like  pattern recognition, image understanding, and cognitive analysis . This type is integral in fields like artificial intelligence and machine learning, where the interpretation of visual data is essential.