Digital image processing involves representing visual information like photos using binary numbers, analyzing and manipulating the image data using algorithms, and applying techniques like enhancement, restoration, segmentation, representation, analysis, and synthesis. Key applications of digital image processing include medical imaging, remote sensing, computer vision, and multimedia.

2. binary representation of visual
information, such as drawings,
pictures, graphs, logos, or
individual video frames
Header + Data
Header – Meta data (Name, type
(extension), Color depth, color
type size)
Data – The pixels in colors as set
by the palette

5. Image enhancement: improving the visual quality of an image, such as
increasing contrast, reducing noise, and removing artifacts.
Image restoration: removing degradation from an image, such as
blurring, noise, and distortion.
Image segmentation: dividing an image into regions or segments, each
of which corresponds to a specific object or feature in the image.
Image representation and description: representing an image in a way
that can be analyzed and manipulated by a computer and describing the
features of an image in a compact and meaningful way.
Image analysis: using algorithms and mathematical models to extract
information from an image, such as recognizing objects, detecting
patterns, and quantifying features.
Image synthesis and compression: generating new images or
compressing existing images to reduce storage and transmission
requirements.
Digital image processing is widely used in a variety of applications,
including medical imaging, remote sensing, computer vision, and
multimedia.