Digital image processing is the use of algorithms and mathematical models to process and analyze digital images. It involves transforming an image into a digital form and performing certain operations to extract useful information from it. The goal of digital image processing is to enhance the quality of images, extract meaningful information from images, and automate image-based tasks. Some of the basic steps involved in digital image processing include image acquisition, image enhancement, image restoration, image compression, and image analysis.
Digital image processing has many advantages over analog image processing, including the use of much more complex algorithms, which can offer both more sophisticated performance at simple tasks and the implementation of methods that would be impossible by analog means. Digital image processing is widely used in a variety of applications, including medical imaging, remote sensing, computer vision, and multimedia.
Some techniques used in digital image processing include anisotropic diffusion, wavelets and multiresolution processing, and color image processing. Digital image processing algorithms can process images much faster than humans, making it possible to analyze large amounts of data in a short amount of time.
