Digital Image Processing

This article explores fundamental concepts and methodologies in digital image processing, a technology widely applied across numerous domains including medical imaging, digital art, and scientific research. The field encompasses diverse techniques for image manipulation, such as image enhancement, filtering, background removal, and edge detection. These methods can be implemented through algorithms like histogram equalization for contrast enhancement, Gaussian/median filtering for noise reduction, and Sobel/Canny operators for edge extraction. Such processing improves image quality by increasing clarity and facilitating analytical workflows. With advancing computational capabilities, image processing has become integral to computer vision and artificial intelligence systems, enabling technologies like facial recognition (implemented through Haar cascades or CNN-based models), autonomous driving (using semantic segmentation algorithms), and robotic vision systems. Consequently, image processing represents a critical technical discipline with growing significance in modern technological ecosystems.