DIGITAL IMAGE FUNDAMENTALS
1. Steps in Digital Image Processing:
Digital image processing involves a series of steps that include image acquisition, image enhancement, image restoration, image compression, and image analysis. These steps can be iterative and may need to be repeated multiple times to achieve the desired results.
2. Components of Digital Image Processing:
The three main components of digital image processing are hardware, software, and algorithms. The hardware includes devices such as cameras and scanners, the software includes applications that process the images, and the algorithms are the mathematical formulas used to manipulate the images.
3. Elements of Visual Perception:
Visual perception involves the interpretation of visual stimuli, such as images. The elements of visual perception include brightness, contrast, color, texture, and spatial relationships. Understanding these elements is important for designing effective image processing algorithms.
4. Image Sensing and Acquisition:
Image sensing involves capturing an image using a device such as a camera or scanner. Image acquisition involves processing the captured image to create a digital image that can be manipulated using software.
5. Image Sampling and Quantization:
Image sampling involves converting a continuous image into a digital image by sampling the image at regular intervals. Image quantization involves assigning a discrete value to each sample based on the number of bits used to represent the pixel values.
6. Relationships Between Pixels:
Pixels in an image are related to one another spatially and in terms of their brightness values. Understanding these relationships is important for designing algorithms that can manipulate the image in meaningful ways.
7. Color Image Fundamentals:
Color images are created by combining red, green, and blue (RGB) channels. The hue, saturation, and intensity (HSI) color model is an alternative model that separates the color information into three components. Understanding color models is important for designing algorithms that can manipulate color images.
8. Two-Dimensional Mathematical Preliminaries:
Two-dimensional mathematical preliminaries include mathematical concepts such as matrices, vectors, and transforms. Understanding these concepts is important for designing algorithms that can manipulate images in the frequency domain.
9. Two-Dimensional Transforms:
Two-dimensional transforms include the discrete Fourier transform (DFT) and the discrete cosine transform (DCT). These transforms are used to convert an image from the spatial domain to the frequency domain, where it can be manipulated using mathematical operations.
Overall, these topics provide a foundation for understanding digital image processing and the various techniques and algorithms used in this field.
Section A: Answer any 5 questions out of 8 (each carrying 5 marks)
1. Define image acquisition and image sensing. Explain the factors that affect image quality during image acquisition.
2. Describe the different image components and their significance in digital image processing.
3. Explain the process of image quantization and sampling. Also, discuss the relationship between pixels in an image.
4. What are the different models for color representation in digital image processing? Describe any one model in detail.
5. What are 2D transforms? Explain DFT with suitable examples.
6. Define spatial resolution and intensity resolution. Explain the effect of increasing or decreasing spatial resolution in digital images.
7. Explain the significance of visual perception in digital image processing.
8. Explain the difference between continuous and discrete signals. How are digital images represented in computer systems?
Section B: Answer any 5 questions out of 8 (each carrying 10 marks)
1. Explain the process of image acquisition using a digital camera. Also, discuss the various factors that affect image quality during acquisition.
2. Describe the concept of quantization and explain the different types of quantization methods used in digital image processing.
3. Explain the different types of color models used in digital image processing. Also, discuss the conversion between RGB and HSI color models.
4. What is the Discrete Fourier Transform (DFT)? Explain the steps involved in computing the DFT of an image.
5. Define image filtering. Explain the difference between spatial domain filtering and frequency domain filtering.
6. Explain the different image enhancement techniques used in digital image processing.
7. What is the relationship between the spatial domain and frequency domain? Explain the Fourier Transform and its properties.
8. Describe the process of image interpolation. Explain the advantages and disadvantages of image interpolation.
Section C: Answer any 5 questions out of 8 (each carrying 15 marks)
1. Explain the different methods used for image acquisition. Also, discuss the advantages and disadvantages of each method.
2. Discuss the different types of color models used in digital image processing. Also, explain the conversion between CMY and RGB color models.
3. Explain the properties of Discrete Fourier Transform (DFT). Also, discuss the difference between DFT and Discrete Cosine Transform (DCT).
4. Define image segmentation. Discuss the different techniques used for image segmentation.
5. Explain the different types of noise that can affect digital images. Also, discuss the techniques used for noise removal in digital images.
6. Explain the concept of image restoration. Discuss the various methods used for image restoration.
7. Define morphological operations in digital image processing. Explain the different types of morphological operations.
8. Discuss the different types of 2D transforms used in digital image processing. Also, explain the properties and applications of each transform.
