1. Introduction to Image Processing

What is Image Processing?

Image processing involves the manipulation and analysis of images through algorithms. It parallels the way humans perceive and interpret visual signals, such as recognizing traffic signals. In the human process, the eyes act as cameras capturing the scene, and the brain interprets these signals to make decisions. In digital image processing, an image is provided as input to a system, where a camera captures the content, and the processing system (analogous to the brain) interprets it. The effectiveness of this interpretation is largely dependent on the algorithms developed for the task, which aim to enhance accuracy.

2. Examples of Image Processing

Image Filters: Averaging and Median Filters

• Original Image with Noise: Noise is an unwanted variation of brightness or color in images.
• Averaging Filter: This filter reduces noise by averaging the pixels within a neighborhood, smoothing the image but potentially blurring edges.
• Median Filter: This filter replaces each pixel with the median value of the neighboring pixels, effectively reducing noise while preserving edges.

Removing Digital Noise

Noise removal is a critical step in preprocessing images for further analysis. It enhances the quality and clarity of images, making subsequent processes like edge detection more accurate.

Edge Detection

Edge detection is vital for identifying object boundaries within images. Techniques include:

• Horizontal and Vertical Edges: Identifying edges based on the direction of intensity change.
• Canny Edge Detection: A multi-stage algorithm that provides robust edge detection by minimizing error rates and accurately detecting edges even in noisy images.

Image Segmentation: Thresholding