Image processing is a field of computer science and electrical engineering that deals with the manipulation and analysis of images. It involves the use of algorithms and techniques to enhance, transform, and extract useful information from images. The primary goal of image processing is to improve the quality of an image for further analysis or to derive meaningful insights.
Image processing has numerous applications, ranging from medical imaging and facial recognition to autonomous vehicles and satellite imagery. Understanding the principles and techniques of image processing is essential for BTech students pursuing degrees in computer science, electronics, and electrical engineering, as it integrates knowledge from mathematics, signal processing, and machine learning.
Image Representation : An image is typically represented as a matrix (2D array) of pixel values. Each pixel contains information about the color or intensity of the image at a specific location.
Image Acquisition : The first step in image processing is capturing an image, usually through cameras, scanners, or sensors.
The image may be captured in different formats (e.g., JPEG, PNG, BMP, TIFF) and resolutions, depending on the specific use case.
Image Preprocessing : Image preprocessing techniques are used to improve the quality of an image and make it suitable for further analysis.
Geometric Transformations : These are operations that modify the spatial arrangement of the pixels in an image
Image Filtering : Spatial Filtering: Involves using a filter (kernel) to process each pixel and its neighbors. This is commonly used for edge detection, sharpening, and blurring (e.g., using a Gaussian filter or Laplacian filter).
Frequency Domain Filtering: Involves transforming the image into the frequency domain using the Fourier Transform, filtering in the frequency domain, and then transforming it back to the spatial domain. This is often used in noise reduction and image enhancement.
