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Table of contents
1.
Introduction
2.
What is Digital Image Processing (DIP)?
3.
Key Stages in Digital Image Processing
4.
Applications of Digital Image Processing
5.
Characteristics of digital image processing
6.
Advantages of digital image processing 
7.
Disadvantages of digital image processing
8.
Frequently Asked Questions
8.1.
Are there any other types of image representation?
8.2.
What is an analog image?
8.3.
What color space do we use for printing images on paper?
9.
Conclusion
Last Updated: Mar 27, 2024
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Digital Image Processing

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Introduction

A digital image is a visual representation of an object, scene, or artwork that is captured and stored in electronic form. Unlike traditional photographs or paintings, which are physical prints or canvases, digital images exist as a collection of data, typically represented in binary code (0s and 1s), that computers can process and display on screens. 

Digital Image Processing

Let us look at how a digital image is processed and represented by computers. 

“Your photography is a record of your living, for anyone who really sees.”
– Paul Strand

In this blog, we will discuss about Digital Image Processing. We will also discuss the stages in digital image processing. We will also explain its characteristics, advantages, and disadvantages. Before moving forward, let us understand what digital image processing is.

What is Digital Image Processing (DIP)?

Digital image processing refers to the manipulation, enhancement, and analysis of digital images using computer algorithms and techniques. It involves the application of various mathematical operations and algorithms to alter or extract information from digital images. In this process, images are treated as two-dimensional arrays of pixels, where each pixel represents a point of color and brightness. 

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Key Stages in Digital Image Processing

 

Steps of DIP

  1. Image acquisition
    It means retrieving the image from a source and giving it a digital form. It is the most fundamental step of image processing. Preprocessing of the image, such as scaling, is also done in this step.
     
  2. Image enhancement
    The basic idea of image enhancement is to bring out the hidden details of an image. It changes certain features of an image such as brightness and contrast.
     
  3. Image restoration
    It deals with the overall up-gradation of an image. The whole appearance of the image is improved in this step.
     
  4. Color image processing
    It includes the processing of colored images. Color image processing is divided into two main parts: full-color processing and pseudo-color processing. Color image processing has gained a lot of popularity due to the use of digital images on the internet.
     
  5. Wavelets and Multi-resolution processing
    It is used to analyze an image in different frequencies at different resolution scales. It reveals an image’s frequency attributes and spatial attributes at the same time.
     
  6. Compression
    It deals with the size of the image. It is used to decrease the storing size of an image. It is very necessary to compress the data for storing or transferring an image over the internet.
     
  7. Morphological processing
    It includes various image processing operations that process the image based on different shapes. It is used to extract different components of an image.
     
  8. Image segmentation
    It is used to break down an image into its constituent parts, known as image segments. It reduces the image’s complexity and makes it easier to process it. It is one of the most difficult phases of image processing.
     
  9. Representation and description
    It is the following step after getting the output from image segmentation. The output of image segmentation is usually in the form of raw pixel data. Representation is used to convert this raw output into another format suitable for processing the image. The description is used to classify one class from the other by extracting information from the image.
     
  10. Object recognition
    Based on the descriptions, labels are attached to different parts of the image. 

    Object recognition in an image
     
  11. Knowledge base
    It is the region of the image where the information of our interest is located in our image.

Applications of Digital Image Processing

  • DIP in the medical field
    DIP is used heavily in the medical field. Some of the examples are:
    → CT Scan
    → PET Scan
    → UV imaging
    → X-Ray
    → Gamma-ray imaging
     

DIP in the medical field

 

  • Image sharpening
    A very common use of DIP is Image sharpening. DIP can be used to sharpen a blurred image to get the desired result.

image sharpening

 

  • Image restoration
    Using DIP, we can restore old images. We can also convert grayscale imaged into RGB format and vice versa.

A restored image

  • Remote sensing
    An application of image processing is remote sensing. An image is taken, processed, and then analyzed.
     
  • Computer vision
    DIP is used in computer vision. Computer vision is used in robot vision and driverless cars.

Computer vision

  • Video processing
    Video processing is done to improve the quality of a video by reducing the noise, fixing the colors, or increasing the frame rate. The fast movement of images forms a video. DIP is used on the images to get a better video.
    Also see, Sampling and Quantization

Characteristics of digital image processing

Digital image processing possesses many characteristics that distinguish it from traditional image processing techniques:

  1. Flexibility: It allows for the application of a wide range of algorithms and techniques to modify images, providing greater flexibility in image enhancement and analysis.
     
  2. Accuracy: It enables precise control over image modifications, ensuring accurate adjustments in brightness, contrast, and other image attributes.
     
  3. Speed: It can be performed swiftly by computers, allowing real-time or near-real-time processing for applications such as video processing and computer vision.
     
  4. Reproducibility: The same image processing algorithm applied to an image will produce consistent results, ensuring reproducibility and consistency in image enhancements.
     
  5. Automation: Many digital image processing tasks can be automated, reducing the need for manual intervention and improving efficiency.

Advantages of digital image processing 

There are several advantages of digital image processing that make it a valuable tool in various fields:

  • It allows for the improvement of image quality, making images more visually appealing and informative.
     
  • It helps in extracting valuable information from images, facilitating scientific analysis and decision-making.
     
  • It enables the detection and recognition of patterns, objects, and features within images, supporting applications like face detection and optical character recognition.
     
  • In medicine, it is essential for diagnosis and analysis in fields like radiology and pathology.
     
  • It allows for efficient compression of images, reducing storage space and facilitating faster transmission over networks.

Disadvantages of digital image processing

While digital image processing offers many advantages, there are some potential disadvantages to consider:

  • Implementing advanced image processing techniques may require specialized knowledge and expertise, making it challenging for non-experts.
     
  • Some image processing algorithms can be computationally intensive, requiring substantial processing power and memory.
     
  • In some cases, aggressive image compression or enhancement may result in a loss of original image information.
     
  • Improper application of image processing techniques can introduce artifacts or distortions in the image.
     
  • In critical applications, relying solely on automated image processing without human validation may lead to errors or incorrect results.

Also Read - Image Sampling

Frequently Asked Questions

Are there any other types of image representation?

Yes, there are other forms of image representation such as 32-bit images, 24-bit images, etc.

What is an analog image?

An analog image is not in quantized form like a digital image. It is represented by continuous variation of image tones.

What color space do we use for printing images on paper?

We mostly use CYMK color space for printing colorful images on paper.

Conclusion

This article talked extensively about image processing and representation. We looked at many different ways by which we can represent an image on the computer.

We looked at the different steps involved in digital image processing and its applications in real life. To understand more about the applications of computer vision, check out this blog. To 

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