Digital Image processing
1.What is an Image?
*Before we jump into image processing, we need to first understand what exactly constitutes an image. An image is represented by its dimensions (height and width) based on the number of pixels. For example, if the dimensions of an image are 500 x 400 (width x height), the total number of pixels in the image is 200000.
*This pixel is a point on the image that takes on a specific shade, opacity or color. It is usually represented in one of the following:
•Grayscale - A pixel is an integer with a value between 0 to 255 (0 is completely black and 255 is completely white).
•RGB - A pixel is made up of 3 integers between 0 to 255 (the integers represent the intensity of red, green, and blue).
•RGBA - It is an extension of RGB with an added alpha field, which represents the opacity of the image.
*Image processing requires fixed sequences of operations that are performed at each pixel of an image. The image processor performs the first sequence of operations on the image, pixel by pixel. Once this is fully done, it will begin to perform the second operation, and so on. The output value of these operations can be computed at any pixel of the image.
2.What is Digital image processing?
Digital image processing is the use of computer algorithms to perform operations on digital images. These operations can include things like image enhancement, restoration, and analysis. Some common techniques used in digital image processing include image filtering, histogram equalization, and edge detection. These techniques can be used for a wide range of applications, including medical imaging, satellite imagery, and machine vision.
Types of digital image processing
*There are several types of digital image processing, including:
#Image enhancement
*This type of processing is used to improve the visual appearance of an image, such as increasing contrast or removing noise.
#Image restoration
*This type of processing is used to restore an image that has been degraded, such as by blur or noise.
#Image analysis
*This type of processing is used to extract useful information from an image, such as detecting edges or measuring object size.
#Image compression
*This type of processing is used to reduce the size of a digital image without significantly degrading its quality.
#Color image processing
*This type of processing is used to enhance, restore or extract information from color images.
#Object recognition and tracking
*This type of processing is used to detect, locate and track objects within an image or a video stream.
#3D imaging
*This type of processing is used to generate 3D representation of an object or a scene from a set of 2D images.
Applications of Digital Image Processing.
1.Medical Image Retrieval
*Image processing has been extensively used in medical research and has enabled more efficient and accurate treatment plans. For example, it can be used for the early detection of breast cancer using a sophisticated nodule detection algorithm in breast scans. Since medical usage calls for highly trained image processors, these applications require significant implementation and evaluation before they can be accepted for use.
2.Traffic Sensing Technologies
In the case of traffic sensors, we use a video image processing system or VIPS. This consists of a) an image capturing system b) a telecommunication system and c) an image processing system. When capturing video, a VIPS has several detection zones which output an “on” signal whenever a vehicle enters the zone, and then output an “off” signal whenever the vehicle exits the detection zone. These detection zones can be set up for multiple lanes and can be used to sense the traffic in a particular station.
Left - normal traffic image | Right - a VIPS image with detection zones (source)
Besides this, it can auto record the license plate of the vehicle, distinguish the type of vehicle, monitor the speed of the driver on the highway and lots more.
3.Image Reconstruction
Image processing can be used to recover and fill in the missing or corrupt parts of an image. This involves using image processing systems that have been trained extensively with existing photo datasets to create newer versions of old and damaged photos.
4.Face Detection
One of the most common applications of image processing that we use today is face detection. It follows deep learning algorithms where the machine is first trained with the specific features of human faces, such as the shape of the face, the distance between the eyes, etc. After teaching the machine these human face features, it will start to accept all objects in an image that resemble a human face. Face detection is a vital tool used in security, biometrics and even filters available on most social media apps these days.
Benefits of Image Processing.
The implementation of image processing techniques has had a massive impact on many tech organizations. Here are some of the most useful benefits of image processing, regardless of the field of operation:
•The digital image can be made available in any desired format (improved image, X-Ray, photo negative, etc)
•It helps to improve images for human interpretation
•Information can be processed and extracted from images for machine interpretation
•The pixels in the image can be manipulated to any desired density and contrast
•Images can be stored and retrieved easily
•It allows for easy electronic transmission of images to third-party providers.
