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Table of contents
1.
Introduction
2.
What is an Image?
2.1.
Types of Images 
3.
Image Processing
3.1.
Steps Involved in Image Processing
4.
Digital Image Processing 
4.1.
Digital Image 
5.
Types of Digital Image Processing
6.
Properties of Digital Image Processing
7.
Real-Life Applications of Digital Image Processing
8.
Advantages of Digital Image Processing
9.
Disadvantages of Digital Image Processing
10.
Frequently Asked Questions 
10.1.
What are the applications of Digital Image Processing?
10.2.
What is Image Reconstruction in Digital Image Processing?
10.3.
What is Blind Deconvolution in Image Processing?
11.
Conclusion
Last Updated: Mar 27, 2024

Digital Image Processing

Author Kartik Singh
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Introduction

Hello Ninjas, while working with an image, we usually need to perform some image-related operations. But do you know what this image is made of? What is image processing, and how is it implemented?

Digital Image Processing

In this article, we will learn about Digital Image Processing. We will understand what an image is. We will understand how image processing steps are implemented in digital image processing.

What is an Image?

An image is a visual representation of something. A digital image is the binary representation of our visual data. The images can be photos, graphics, or even video frames. Image can be represented as a 2-D function in x and y coordinates. The function is defined such that for any pair of (x,y), we have a function value determining the intensity at that point.

Image as Matrix

While talking about digital images, the value for the function at (x,y) is finite. Digital images are finite-sized images. The digital elements that make up a digital image are called pixels. The pixel value denotes the intensity at a particular point in the image.

Types of Images 

Broadly images are classified into the following categories.

  • Binary Image - The images with pixel values of 0 and 1 are called binary images. 0 corresponds to black, and one corresponds to white in a binary image.
     
  • Gray-Scale Images - The images here have gray-level information. No colors are associated with these images. The number of bits used determines the number of gray levels. The typical gray-scale image consists of 8 bits/pixel data. Therefore each pixel can have values in the range of 0 to 255.
     
  • Color Images - The images are considered 3-band monochrome images. Here the different bands correspond to a different color. The pixel value is stored for each spectral band, like gray-scale images. The typical color image has red, green, and blue spectral bands. 8-bits are associated with each spectral band. This image would have 24 bits/pixel.
     
  • Multispectral Images - The images that are outside the range of human perception. The images are visually represented by mapping the different spectral bands to The RGB components.
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Image Processing

Image processing is the process of transforming an image into a digital image, and various operations are performed on it. The operations are performed to extract some useful information from the image. It makes use of several algorithms to analyze the images.

Using image processing, we can extract some essential information from the image. We can enhance the image quality. Image processing allows the machine to interpret images more clearly.

Steps Involved in Image Processing

The following are some fundamental steps that are involved in image processing:

  • Image Acquisition - It is the first step in image processing. It is also called the pre-processing phase. 
     
  • Image Enhancement - It is the process of highlighting the essential features in an image. It involves changing contrast, brightness, etc.
     
  • Image Restoration - It is the process of improving the appearance. It is done using mathematical models.
     
  • Color Image Processing - This includes processing using several coloring models.
     
  • Wavelet and Multiresolution Processing - The images are divided into smaller regions for data compression. Here, the wavelet represents an image in various degrees of resolution.
     
  • Compression- It is the process of reducing the storage required to save an image. It is done to make a smooth transition over the internet. 
     
  • Morphological Processing- It is the process of morphing images based on the shapes.
     
  • Segmentation - It is the process of partitioning the image into smaller objects.
     
  • Representation and Description - After segmentation, the sub-regions are represented and described for further processing.
     
  • Recognition- It is a process of labeling a sub-region of an image based on its description.

Digital Image Processing 

Digital Image processing is the manipulation of images. We use mathematical algorithms to manipulate the images. It involves using software that can implement mathematical algorithms. Popular software platforms used are - Adobe Photoshop, MATLAB, etc.

We can also convert the signals from image sensors to digital images in Digital Image Processing. The process used in digital image processing is the operations of image processing. Digital Image Processing deals with signals, images, graphics, pixels, motion sensing, and video frames.

It is a subset of image processing and provides the image processing operations. The main digital image processing operations include - image enhancing and image signal processing. It can deal with analog, digital signals, and even voice signals.

Digital Image 

The digital image is an image represented using a finite function of x and y coordinates.

The digital elements that make up a digital image are called pixels. The pixel value denotes the intensity at a particular point in the image. 

Image as Function

The image index starts with one in MATLAB and goes to a finite value in both coordinates. Thus, a digital image can be represented as above.

Types of Digital Image Processing

The type of Digital Image Processing depends on the task associated with it. For instance, the following are some common types of Digital Image Processing.

  • Remote Sensing - Here, the application of Digital Image Processing is in satellites. The satellites use the image of the earth and process them to see the activities from space.
     
  • Healthcare Utility - Digital Image Processing finds a crucial role in the healthcare field. The applications are in CT-Scans, PET scans, UV imaging, Ultrasounds, and X-rays.
     
  • Image Enhancement - Digital image processing includes the processes like image smoothing, edge sharpening, and image restoration, which are useful in image enhancement.
     
  • Robotic Vision - Digital Image Processing allows robots and machines to perceive the surrounding environment just like humans.

Properties of Digital Image Processing

The following are some of the properties associated with Digital Image Processing.

  • It is a digital representation of an image using a finite mathematical function.
     
  • It uses some software for performing functions.
     
  • The software is easily available, and most of them are free of cost.
     
  • It involves the various fundamental steps of image processing ( discussed above).
     
  • It is used to reduce the complexity of the image. It makes it better for the user to understand the image.
     
  • It is used to get an enhanced image and has many applications.

Real-Life Applications of Digital Image Processing

Digital Image processing is an evolving technology. It has been implemented in many areas along with other technologies to give robust results. The following are some of the significant real-life applications of digital image processing.

  • Medical Field - Digital Image processing is extensively used in Medical Field. It can be used for the early detection of cancer and tumors by nodule detection algorithms which can be applied to the scan images of the human body.
     
  • Traffic Congestion Control - Digital image processing can be used to read traffic images and videos. The input images can be used to detect traffic and plan efficient solutions to control the traffic.
     
  • Face Detection - Digital Image Processing is now used for face recognition. This process involves the training of deep neural networks. Digital Image Processing is used to preprocess the input images, and then the input is given to the deep neural networks.
     
  • Image Restoration - Digital Image Processing methods are used to remove noise and fill in the missing parts in an image. It makes the image more clear and is used to restore damaged photos.

Advantages of Digital Image Processing

The following are some of the major advantages of Digital Image Processing.

  • Digital Image processing involves image restoration and image enhancement that are used to improve the image quality.
     
  • The processed image can be used with other libraries for detection and recognition.
     
  • It provides a feature with a controlled action on the view of the image, like - zooming and windowing.
     
  • It is a fast and efficient way of performing image processing over an image.
     
  • It provides the flexibility of sharing images. The image can be compressed and are easy to share over the internet.

Disadvantages of Digital Image Processing

  • Sometimes the free software may not provide all the necessary operations.
     
  • Handling and using Digital Image Processing requires the involvement of a qualified person.
     
  • Sometimes the tasks are long, and the process can be very time-consuming.
     
  • The mathematical algorithms are limited. So the operations provided are constrained.

Frequently Asked Questions 

What are the applications of Digital Image Processing?

Digital Image Processing has various real-world applications. The applications include- Remote sensing, Robotic Vision, the healthcare field - Liver CT image segmentation, motion sensing, and video frame processing for caption generation.

What is Image Reconstruction in Digital Image Processing?

Image reconstruction is the process of recovering and filling the missing parts in an image. It involves using an extensive dataset to create new and clear pictures from old and damaged photos.

What is Blind Deconvolution in Image Processing?

Blind deconvolution is a technique to recover a clear image from a blurry, noisy image. Here we need to find out how the image was blurred. The image might be blurred due to defocus or camera movement during image capture.

Conclusion

In this article, we learned about the concept of an image. We saw the representation of a digital image and the fundamental steps of image processing. Lastly, we discussed Digital image processing, its characteristics, advantages, and disadvantages.

To learn more about Digital Image Processing, you can check out our other blogs:

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