Image Processing

In this lesson, we introduce the fundamentals of image processing, explaining how images are structured and demonstrating the use of filters to modify their appearance. This process is instrumental in highlighting specific features within an image.

Representing Images

An image is essentially an array of pixel values. For a grayscale image, each pixel value represents a shade of gray ranging from 0 (black) to 255 (white). On the other hand, color images are represented by three separate channels—red, green, and blue—with each channel containing its own array of pixel values.

Applying Filters

Image processing techniques often rely on applying filters. Filters are typically composed of a kernel, which is a small matrix of weights. This kernel is convolved over the image: it moves across the image and, at each position, computes a new pixel value by combining the original pixel values with the kernel’s weights.

The convolution process results in a modified version of the original image. For example, certain areas of the image may show concentrations of pixel values like 255 (white), while others may display 0 (black), thereby emphasizing distinct visual features.

Example: Laplace Filter

A common filter used in image processing is the Laplace filter, which is highly effective for edge detection. As the Laplace kernel moves over the image, it accentuates regions with abrupt changes in pixel intensity, thus clearly defining the edges of objects within the image.

The output of this filtering process is an image where edges are prominently defined. Regions with minimal variation—such as the image center—may remain at a uniform value of 0. This clear delineation of features makes subsequent analysis and computer vision tasks more effective.

Next, we will explore how Convolutional Neural Networks build upon these image processing techniques to further analyze and interpret visual data.