Image Representation,Digital Image Processing: Problems and application

The term "image representation" describes the metadata and digital storage of images. This is transforming visual data from photographs into a digital or mathematical structure that computers can process, comprehend, and evaluate. Several approaches and strategies are employed in the representation of images, such as:

• Pixel-based representation
• Vector-based representation
• Feature-based representation
• Transform-based representation
• Color-based representation

 Digital Image Processing

Digital image processing is the act of using a digital computer to process digital images. Another way to describe it is that computer algorithms are being used to either extract some important information or create an upgraded images

The basic steps involved in digital image processing are:

• Image acquisition is the process of taking a picture with a digital camera or scanner, or it's the process of importing an already-existing picture into a computer.
• Image enhancement is the process of making an image look better by enhancing its contrast, lowering its noise level, and getting rid of artifacts.
• Image restoration is the process of restoring a degraded image, including noise, distortion, and blurring.
• Image segmentation is the process of breaking an image up into areas or segments, each of which represents a different object or feature.
• Image description and representation this entails characterizing an image's features succinctly and meaningfully, as well as representing an image in a fashion that allows for computer analysis and manipulation.
• Image synthesis and compression this involves 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.
• Image analysis this entails extracting data using mathematical models and algorithms.

Problems

• Noise Removal
• Enhancing image quality by improving contrast, brightness, or emphasizing certain features.
• Recovering an image that has been degraded by factors such as blurring, motion, or noise.
• Dividing an image into meaningful regions or objects.
• Recognizing and categorizing things in a picture.
• Processing aerial or satellite photos for disaster management, urban planning, and environmental monitoring.

Applications

• Medical Imaging
• Remote Sensing
• Surveillance and Security
• Industrial Inspection
• Autonomous Vehicles
• Entertainment and Media
• Document Processing
• Astronomy