Image processing is quite familiar in computer science, especially in computer vision.
Digital image processing uses algorithms to extract useful information, such as objects in the image or images.
Digital image processing has many advantages over analog image processing. Why? Because analog images have continuous properties, for example, appearances on television monitors, X-ray photos, photos printed on photo paper, paintings, CT scan results, etc.
Analog images cannot be represented on a computer, so they cannot be processed directly, unlike digital photos, which can be manipulated using a computer. In this article, will discuss the basic steps in image processing.
What is Image Processing?
Image Processing is a method for processing images (Images) into digital form for a specific purpose. In the beginning, image processing served to improve and improve the quality of an idea.
Still, with the times and the emergence of computational sciences, humans can retrieve any information in an image. The input is an image (image), and the output is an enhanced image.
For example, an image that is less sharp in color, blurry, and has noise (e.g., white spots) requires improving image quality to obtain better information.
Pixels in The Image
The image consists of several elements, each of which has a specific value. These elements are referred to as image elements or pixels.
A pixel is a point in an image that takes on a particular shade, opacity, or color. Based on the color type, digital photos are grouped into:
1. Binary image: pixels consisting of the numbers 0 (black) and 1 (white)
2. Grayscale image: pixels consisting of integers with values between 0 and 255 (0 is completely black, and 255 is entirely white).
3. RGB image: pixel intensity is composed of three color channels, namely red, green and blue, with a value range between 0 to 255.
4. RGBA image: extended RGB with the addition of an alpha field, representing the image’s opacity or darkness.
Image Processing Objectives
Image processing is generally defined as manipulating images for aesthetic purposes or supporting mere ‘beauty/beauty’ standards.
Even though the real meaning is a method for translating or equating perceptions between the human visual system and digital image devices.
This is because the human visual system is different from the digital computer system. Digital image systems impose noise and bandwidth restrictions.
The primary purpose of image processing is to convert images into digital form and perform operations with specific algorithms to obtain models or extract useful information from images.
The general purpose of image processing is as follows:
- Visualization: find objects that are not visible in the image
Image sharpening and recovery: used for better image resolution. - Image capture: search for images from a digital database similar to the original image.
- Pattern metering: measuring various patterns around objects in an image
- Image recognition: every object in the image can be distinguished.
Basic Steps in Image Processing
The following are some basic steps in image processing or image processing.
1. Image Acquisition
The acquisition is the first essential step in image processing. Image acquisition is basically taken from image capture.
This step is also known as preprocessing in image processing. It involves preprocessing methods such as scaling, color conversion (RGB to grayscale), etc.
2. Image Enhancement
Image enhancement is the process of filtering images (removing noise, increasing contrast, etc.) to improve the quality of the picture. The resulting image will be more by the original embodiment.
3. Image Restoration
This step is the process of improving an image’s appearance (reducing blur, etc.) by means of a mathematical, probabilistic, or image degradation model.
4. Color Image Processing
Color image processing is an essential field because of the significant increase in the use of images. This section takes care of processing color images as indexed or RGB images.
5. Multiresolution Processing
This stage is the process of representing images in various levels of resolution. The idea is divided into smaller regions for data compression and pyramidal representation.
Wavelet and multiresolution processing is a process that represents an image in several resolutions.
6. Image Compression (Compression)
This stage involves techniques to reduce the image size with minimum loss in quality.
Compression relates to techniques for reducing the storage required to store images or the bandwidth to transmit them. Especially on the internet, it is necessary to compress data.
7. Morphological Processing
This stage is the process of extracting image components that are useful in representing and describing shapes. Morphological Processing, namely the process of obtaining information that states a description of a shape in an image.
8. Image Segmentation (Segmentation)
Segmentation is one of the most challenging image-processing steps. This is the process of partitioning the image into several segments and distinguishing or separating objects in an image, such as objects from their background.
9. Representation and Description
This stage follows the output of the segmentation stage, selecting the only representation of the solution to transform raw data into processed data.
This involves the representation of images in various forms. Boundary representation: focuses on external shape characteristics such as angles and inflections. Regional representation: focuses on internal properties such as texture and skeletal form.
10. Image recognition (Recognition)
This stage is assigning labels to objects based on their descriptions.
Recognition is a process that assigns labels, such as “vehicles,” to objects based on their descriptors or a process that is carried out to recognize what objects are in an image.
11. Implementation of Image Processing
The implementation of image processing has been widely used, and we can find it in real life. Here are some examples of the performance of image processing:
- Machines or robots
- Traffic Sensing Technology
- Image fix
- Face detection, etc
Image Processing Applications
Image processing applications examples include:
1. Medical Image Retrieval
Medical research has heavily utilized image processing to improve treatment plans through increased efficiency and accuracy. One example is utilizing advanced algorithms for early detection of breast cancer in scans.
However, due to the specialized nature of these applications, significant effort is required for proper implementation and evaluation before they can be adopted for medical use.
2. Traffic Sensing Technologies
A VIPS (video image processing system) is used for traffic sensors. It is composed of three main components: an image capturing system, a telecommunication system, and an image processing system.
The VIPS can detect vehicles in specific areas, known as detection zones, by outputting an “on” signal when a vehicle enters the zone and an “off” signal when it exits.
This system can be set up for multiple lanes and can be used to monitor traffic at a specific location.
3. Image Reconstruction
Image processing can be used to restore and repair missing or damaged parts of an image. This is achieved by using image processing systems that have been extensively trained on large datasets of photos to generate new versions of old or corrupted images.
4. Face Detection
One prevalent application of image processing is face detection. It utilizes deep learning algorithms that train the machine to recognize specific characteristics of human faces, such as facial shape and the distance between eyes.
Once the machine has learned these features, it can identify objects in an image that resemble a human face. Face detection is widely used in security, biometrics, and even in filters on social media apps.
Image Processing Benefits
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