What are the Applications and Problems of Digital Image Processing. Since digital image processing has very wide applications and almost all of the technical fields are impacted by DIP, we will just discuss some of the major applications of DIP.
Digital Image processing is not just limited to adjust the spatial resolution of the everyday images captured by the camera. It is not just limited to increase the brightness of the photo, e.t.c. Rather it is far more than that.
Applications of Digital Image Processing
Some of the major fields in which digital image processing is widely used are mentioned below
- Image sharpening and restoration
- Medical field
- Remote sensing
- Transmission and encoding
- Machine/Robot vision
- Color processing
- Pattern recognition
- Video processing
- Microscopic Imaging
Image sharpening and restoration
Image sharpening and restoration refers here to process images that have been captured from the modern camera to make them a better image or to manipulate those images in way to achieve desired result. It refers to do what Photoshop usually does.
This includes Zooming, blurring , sharpening , gray scale to color conversion, detecting edges and vice versa , Image retrieval and Image recognition.
The common applications of DIP in the field of medical is
- Gamma ray imaging
- PET scan
- X Ray Imaging
- Medical CT
- UV imaging
In the field of remote sensing , the area of the earth is scanned by a satellite or from a very high ground and then it is analyzed to obtain information about it. One particular application of digital image processing in the field of remote sensing is to detect infrastructure damages caused by an earthquake.
As it takes longer time to grasp damage, even if serious damages are focused on. Since the area effected by the earthquake is sometimes so wide , that it not possible to examine it with human eye in order to estimate damages. Even if it is , then it is very hectic and time consuming procedure. So a solution to this is found in digital image processing. An image of the effected area is captured from the above ground and then it is analyzed to detect the various types of damage done by the earthquake.
The key steps include in the analysis are
- The extraction of edges
- Analysis and enhancement of various types of edges
Transmission and encoding
The very first image that has been transmitted over the wire was from London to New York via a submarine cable. The picture that was sent is shown below.
The picture that was sent took three hours to reach from one place to another.
Now just imagine , that today we are able to see live video feed , or live cctv footage from one continent to another with just a delay of seconds. It means that a lot of work has been done in this field too. This field doesnot only focus on transmission , but also on encoding. Many different formats have been developed for high or low bandwith to encode photos and then stream it over the internet or e.t.c.
Apart form the many challenges that a robot face today , one of the biggest challenge still is to increase the vision of the robot. Make robot able to see things , identify them , identify the hurdles e.t.c. Much work has been contributed by this field and a complete other field of computer vision has been introduced to work on it.
Hurdle detection is one of the common task that has been done through image processing, by identifying different type of objects in the image and then calculating the distance between robot and hurdles.
Line follower robot
Most of the robots today work by following the line and thus are called line follower robots. This help a robot to move on its path and perform some tasks. This has also been achieved through image processing.
Color processing includes processing of colored images and different color spaces that are used. For example RGB color model , YCbCr, HSV. It also involves studying transmission , storage , and encoding of these color images.
Pattern recognition involves study from image processing and from various other fields that includes machine learning ( a branch of artificial intelligence). In pattern recognition , image processing is used for identifying the objects in an images and then machine learning is used to train the system for the change in pattern. Pattern recognition is used in computer aided diagnosis , recognition of handwriting , recognition of images e.t.c
A video is nothing but just the very fast movement of pictures. The quality of the video depends on the number of frames/pictures per minute and the quality of each frame being used. Video processing involves noise reduction , detail enhancement , motion detection , frame rate conversion , aspect ratio conversion , color space conversion e.t.c.
What are the current Issues/Problems in Image Processing
Consider Segmentation problem
You will never get satisfied with the results obtained ! (I have never heard 100% results in Image processing)
Hard Coded / No generic solutions
Consider a filtering problem or a contrast enhancement problem which needs to be solved generic but varies according to many factors (source, kernel, methods, etc) resulting in hard coding.
More number of ways and many possible solutions for a problem. For instance you need boundary of a object -> start with edge detection (you have around 5 edge detection algorithms) and end up somewhere near skeletonization or distance transform.
Start to work an application -> it is obvious in IP you are not going to attain 100% accuracy -> continue to improve the accuracy