Image processing techniques are widely applied in various industries, including medical imaging, security, military, and general image enhancement, to improve image quality and extract useful information. In medical imaging, these techniques assist in identifying anomalies by producing clear images, while in security, they enhance camera footage for better monitoring. The military uses image processing for steganography to covertly transmit information. General applications involve image sharpening and restoration using tools like Photoshop or social media filters. Machine learning amplifies these processes by automating tasks and maintaining image quality, employing advanced techniques like Convolutional Neural Networks (CNN) for tasks like object detection and image segmentation. Data augmentation, crucial for machine learning, is achieved through transformations such as flipping, rotating, and zooming, enhancing dataset diversity without additional data collection. Moreover, the article distinguishes between image processing and computer vision, emphasizing that while image processing focuses on transforming images, computer vision helps computers understand and derive meaning from images.