Technical Methods and Applications of Digital Image Processing

Technical Methods and Applications of Digital Image Processing

Digital Image Processing is a method and technology for removing noise, enhancing, restoring, segmenting, and extracting features through a computer.
Published: Sep 23, 2022
Technical Methods and Applications of Digital Image Processing

The so-called digital image processing refers to the process of converting image signals into digital signals and processing them by computer. In image processing. Low-quality images are input, and images with improved quality are output. Commonly used image processing methods include image enhancement, restoration, encoding, and compression.

What are the Common Technical Methods of Digital Image Processing?

  • Image encoding and compression:
    Image coding and compression techniques reduce the amount of data (ie, the number of bits) that describe an image, save image transmission, and processing time and reduce the amount of memory occupied. Compression can be achieved without distortion or with distortion allowed. Coding is the most important method in compression technology, and it is the earliest and relatively mature technology in image processing technology.
  • Image transformation:
    Since the image array is large, processing it directly in the spatial domain involves a large amount of computation. Therefore, various image transformation methods or indirect processing techniques are often used to convert the processing in the spatial domain into the processing in the transform domain. Not only reduces the amount of computation but also allows for more efficient processing. Wavelet transform has good localization properties in both time and frequency domains and has extensive and effective applications in image processing.
  • Image description:
    The image description is a prerequisite for image recognition and understanding. As the simplest binary image, its geometric characteristics can be used to describe the characteristics of the object. The general image description method adopts two-dimensional shape description, which has two methods: boundary description and region description. For special texture images, a two-dimensional texture feature description can be used. With the in-depth development of image processing research, three-dimensional object description has been studied. Such as volume description, surface description, and generalized cylinder description have been proposed.
  • Image segmentation:
    Image segmentation is one of the key technologies in digital image processing. Image segmentation is to extract meaningful feature parts in an image, such as edges and regions in the image, which are the basis for further image recognition, analysis, and understanding.
  • Image enhancement and restoration:
    The purpose of image enhancement and restoration is to improve the quality of the image, such as by removing noise and improving the clarity of the image. Image enhancement highlights the parts of interest in the image regardless of the cause of image degradation. If the high-frequency components of the image are enhanced, the outlines of objects in the image can be clear and the details are obvious. If the low-frequency components are enhanced, the influence of noise in the image can be reduced. Image restoration requires a certain understanding of the reasons for image degradation. A degradation model should be established according to the degradation process, and then a certain filtering method should be used to restore or reconstruct the original image.
  • Image classification:
    Image classification belongs to the category of pattern recognition, and its main content is to perform image segmentation and feature extraction after some preprocessing (enhancement, restoration, compression) of the image, to carry out decision classification. Image classification often uses classical pattern recognition methods, including statistical pattern classification and syntactic (structural) pattern classification. In recent years, the newly developed fuzzy pattern recognition and artificial neural network pattern classification have received more and more attention in image recognition.
  • Histogram equalization:
    Converting an image into another image with a balanced histogram through grayscale transformation is the process of having the same number of pixels in a certain grayscale range.
  • Addition and subtraction of images:
    The addition and subtraction operation of two images is to perform addition and subtraction operations on the grayscale values on the storage rectangle point column corresponding to the images. Image addition can add the content of one image to another image, can achieve double exposure, and can average multiple images of the same scene, which can reduce noise. Image subtraction can be used for motion detection or to remove unwanted additive patterns in images.
  • Common denoising methods:
    It mainly uses filters to filter noisy images, such as arithmetic mean filtering, median filtering, etc.

What are the Basic Properties of an Image?

  • The brightness of an image:
    Also known as grayscale, it is the change in light and shade of color, usually expressed from black to white from 0% to 100%.
  • Image contrast:
    The ratio of the black and white of the picture, that is, the gradient level from black to white. The larger the ratio, the more gradient levels from black to white, and the richer the color expression.
  • Histogram:
    Indicates the number of pixels with each gray level in the image, reflecting the frequency of each gray level in the image. The storage form of the image in the computer is like a matrix with many points. These points are arranged neatly in rows and columns. The value on each point is the gray value of the image, and the histogram is the gray value of each gray at this point. The number of occurrences in the matrix.
  • Image noise:
    Just like hearing, when there is a lot of noise around, it will affect our ability to hear the content. Similarly, for images, we can see an image clearly, but sometimes there are some patterns on the image that we don’t need so we can’t see a picture clearly, this is the image noise.

What is 3D Image Processing?

3D image processing includes the visualization, processing, and analysis of 3D image data, such as data obtained from magnetic resonance imaging (MRI) and computed tomography (CT), undergoing transformation, filtering, image segmentation, and morphological manipulation. Using these image data, it is possible to quantitatively assess the real structure through a computer simulation process.

What problems can 3D image processing solve?

With the help of 3D image processing, it is possible to build models of extremely complex structures, such as human anatomy, the microstructure of material samples, or the inherent defects of industrial components. By creating accurate scanned digital models of objects, many challenging problems can be solved using structural analysis and simulation, such as patient-tailored implant design, material design optimization with targeted properties, or the fabrication of high-value components.

How does 3D image processing work?

Raw data acquired from a CT or MRI scanner must first be converted into tomographic images through a reconstruction process before the images can be better interpreted and understood. This is usually done in the software that came with the scanning device. Whether CT or MRI, the output is a three-dimensional bitmap of grayscale intensities, a grid of voxels (three-dimensional pixels). In a CT scan, the grayscale intensity at a particular voxel is related to the subject's absorption of X-rays at that location, while in an MRI machine, it is related to protons emitted during relaxation after applying a strong magnetic field. The signal intensity is related, and different tissues will have different proton concentrations, so different grayscale intensities appear in the image.

What are the Application Areas of Digital Image Processing?

  • Aerospace and aviation:
    Application in aerospace and aviation technology the application of digital image processing technology in aerospace and aviation technology, in addition to JPL's processing of photos of the moon and Mars, on the other hand, is in aircraft remote sensing and satellite remote sensing technology. Many countries send out many reconnaissance planes every day to take a lot of aerial photography of areas of interest on Earth. The processing and analysis of the resulting photos, which previously required the employment of thousands of people, are now interpreted, and analyzed by image processing systems equipped with advanced computers, saving manpower and speeding up the extraction of images from photos. A large amount of useful information cannot be found by human beings.
    These images are first processed (digitized, encoded) into digital signals in the air and stored in tapes. When the satellite passes over the ground station, they are transmitted at high speed and then analyzed and interpreted by the processing center. Whether these images are imaged, stored, transmitted, or analyzed for interpretation, many digital image processing methods must be used. Now countries around the world are using the images obtained by satellites to conduct resource surveys (such as forest surveys, marine sediment, and fishery surveys, water resources surveys, etc.), disaster detection (such as disease and insect pest detection, water and fire detection, environmental pollution detection, etc.), resource Exploration (such as petroleum exploration, mineral production detection, geographical location exploration, and analysis of large-scale projects, etc.), agricultural planning (such as soil nutrition, moisture, and crop growth, yield estimation, etc.), urban planning (such as geological structure, water source, and environmental analysis, etc).
  • Biomedical engineering:
    The application of digital image processing in biomedical engineering is extensive and effective. In addition to the CT technology described above, there is also the processing and analysis of medical microscopic images, such as red blood cell and white blood cell classification, chromosome analysis, and cancer cell identification. In addition, image processing technology is widely used in medical diagnoses such as X-ray lung image enhancement, ultrasonic image processing, electrocardiogram analysis, and stereotactic radiotherapy.
  • Communication engineering:
    The main development direction of current communication is multimedia communication which combines voice, text, image, and data. Specifically, the telephone, television, and computer are transmitted on the digital communication network in the way of triple play. Among them, image communication is the most complicated, because the data volume of the image is huge, such as the transmission rate of color TV signals being more than 100Mbit/s. To transmit such high-rate data in real-time, coding technology must be used to compress the bit amount of information. In a sense, encoding compression is the key to the success or failure of these technologies.
  • Industrial and engineering aspects:
    Image processing technology has a wide range of applications in industry and engineering, such as inspecting the quality of parts and classifying them in automatic assembly lines, an inspection of printed circuit board defects, stress analysis of elastic photos, resistance, and fluid dynamics photos. Lift analysis, automatic sorting of postal letters, identifying the shape and arrangement of workpieces and objects in some toxic and radioactive environments, using industrial vision in advanced design and manufacturing technology, etc. It is worth mentioning that the development of intelligent robots with visual, auditory, and tactile functions will bring new incentives to industrial and agricultural production. At present, it has been effectively used in painting, welding, and assembly in industrial production.
  • Military and public security:
    In the military aspect, image processing and recognition are mainly used for accurate terminal guidance of missiles, interpretation of various reconnaissance photos, military automation command systems with image transmission, storage and display, and simulation training systems for aircraft, tanks, and warships. Interpretation and analysis of public security business pictures, fingerprint identification, face identification, restoration of incomplete pictures, traffic monitoring, accident analysis, etc.
  • Cultural and artistic aspects:
    At present, such applications include digital editing of TV pictures, animation production, electronic image games, textile handicraft design, clothing design and production, hair design, reproduction, and restoration of photos of cultural relics, analysis and scoring of athletes' movements, etc.
  • Robot Vision:
    As an important sensory organ of intelligent robots, machine vision mainly performs three-dimensional scene understanding and recognition, and is an open topic currently under research. Machine vision is used in military reconnaissance, autonomous robots in dangerous environments, intelligent robots in postal, hospital, and home services, assembly line workpiece identification and positioning, automatic operation of space robots, etc.
  • Video and multimedia systems:
    Image processing, transformation, and synthesis are widely used in TV production systems, acquisition, compression, processing, storage, and transmission of still images and dynamic images in multimedia systems.
  • Scientific visualization:
    Image processing and graphics are closely combined to form new research tools in various fields of scientific research.
  • E-commerce:
    Such as identity authentication, product anti-counterfeiting, watermarking technology, etc.
Published by Sep 23, 2022 Source :synopsys, Source :read01

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