Digital Image Processing

Introduction

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing.

History

The origins of digital image processing can be traced back to the 1920s, with the development of television systems. The first successful digital image processing system was developed in the late 1960s by researchers at the Jet Propulsion Laboratory (JPL). This system was used to enhance images of the moon for the Apollo missions.

Fundamentals of Digital Image Processing

Digital image processing involves the manipulation of digital images through an algorithm. This is done in order to enhance the image or extract useful information from it.

Image Acquisition

Image acquisition is the first step in the digital image processing framework. The image is captured by a sensor (such as a camera) and converted into digital form using a analog-to-digital converter.

Image Enhancement

Image enhancement techniques are used to process an image so that the result is more suitable for a specific application. This process does not increase the inherent information content in data, but it increases the dynamic range of the chosen features so that they can be detected easily.

Image Restoration

Image restoration is a process that is aimed at removing noise from the digital image. It is subjective, just like image enhancement, and its aim is to improve the visual quality of the image.

Color Image Processing

Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images on the Internet. It involves the manipulation of colored images to enhance visual perception, and the interpretation and understanding of scene content.

Wavelets and Multiresolution Processing

Wavelets are a tool that decompose images into different frequency subbands. This allows for the isolation of the image components that are compact in spatial frequency domain. Multiresolution processing is a pyramid scheme where approximation images are created by successively reducing the sampling rate.

Image Compression

Image compression addresses the problem of reducing the amount of data required to represent a digital image. The underlying basis of the reduction process is the removal of redundant data.

Morphological Processing

Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape.

Image Segmentation

Image segmentation is the process of partitioning a digital image into multiple segments to simplify the image into something that is more meaningful and easier to analyze.

Representation and Description

Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region or all the points in the region itself.

Object Recognition

Object recognition is a process that assigns a label to an object based on its descriptors. It is the process that assigns symbolic identity to the detected objects.

Applications

Digital image processing has a wide range of applications, from medicine and forensics to astronomy and art. It is used in various fields to improve image quality, search for information, and extract features.