Image Processing: Difference between revisions

Latest revision as of 09:38, 6 July 2025

Image Processing is a method of manipulating digital images through the use of algorithms and software techniques. The primary goal of image processing is to enhance the quality of an image or to extract useful information from it. This field encompasses a range of techniques that can be applied in various domains, including computer vision, medical imaging, remote sensing, and more. As technology progresses, image processing techniques are becoming increasingly sophisticated and widely applied in industries such as entertainment, healthcare, and security.

Background or History

The origins of image processing can be traced back to the early 1960s when researchers began to experiment with the manipulation and analysis of images through computers. Early methods focused primarily on simple geometric transformations and basic enhancements. The introduction of the digital computer played a significant role in the evolution of this field, allowing for more complex algorithms to be developed.

1960s to 1970s

During the 1960s, pioneers like Lawrence Roberts and others began developing mathematical models for image analysis, laying the groundwork for future advancements in image processing. At this time, systems were primarily used in research laboratories and were characterized by their limited capabilities.

The 1970s saw the advent of more sophisticated techniques, including edge detection, filtering, and pattern recognition. Researchers such as David Marr were instrumental in formulating theories about the human visual system, which influenced the development of algorithms for image processing. Notably, the development of computer graphics also bolstered the field, providing new avenues for visual representation.

1980s to 1990s

The 1980s marked a significant shift in image processing with the introduction of digital cameras and the increased availability of digital image data. This era also saw the emergence of specialized software such as Adobe Photoshop, which put image processing tools into the hands of artists and general users. Algorithms that utilized techniques like Fourier transforms became commonplace, facilitating advancements in frequency domain analysis.

By the 1990s, the field matured, leveraging the rise of computers and digital imaging technologies. The introduction of new image formats and standards allowed for greater interoperability and quality in image sharing. Image processing began being integrated into various applications including telecommunications, medical imaging, and automated inspection systems.

2000s and Beyond

With the advent of the 21st century, image processing entered a new era characterized by the explosion of digital content and the availability of powerful computational resources. Machine learning and artificial intelligence began to revolutionize image processing, enabling sophisticated applications such as facial recognition and image classification. Technologies like deep learning and convolutional neural networks (CNNs) paved the way for remarkable advancements in automated image analysis.

In contemporary use, image processing techniques are an integral part of smartphone applications, social media platforms, and autonomous systems. The integration of image processing with machine learning continues to expand its capabilities, leading to new developments in augmented reality, 3D imaging, and beyond.

Architecture and Design

Image processing systems primarily consist of hardware and software components that work together to perform various tasks. The architecture often varies based on the specific application and the complexity of the processing tasks.

Hardware Components

The hardware utilized in image processing includes processors, cameras, sensors, and memory storage systems. High-performance processors, such as Graphics Processing Units (GPUs), are common in modern image processing applications due to their ability to handle parallel processing efficiently. Additionally, specialized hardware like Field Programmable Gate Arrays (FPGAs) may be used for real-time image processing tasks.

Software Components

At the software level, image processing relies on a variety of programming languages and libraries. Languages such as Python, C++, and MATLAB are frequently utilized because of their robust libraries and frameworks like OpenCV and TensorFlow, which provide tools to implement complex algorithms. Image processing software typically includes functionalities like image filtering, segmentation, feature extraction, and compression techniques.

Algorithms and Techniques

Central to image processing are various algorithms that are used to enhance, modify, and analyze images. Common techniques include:

**Image enhancement**: Techniques such as histogram equalization and contrast stretching improve the visual quality of an image.
**Image restoration**: Algorithms that remove noise or correct features for a clearer representation of the original scene, such as deblurring techniques.
**Image segmentation**: Methods that partition an image into sections or objects, which can facilitate later analysis and understanding.
**Feature extraction**: Techniques that identify and isolate significant features or patterns within an image, often used in machine learning tasks.

These algorithms serve as the backbone of image processing applications and can be combined in various ways to achieve desired outcomes.

Implementation and Applications

Image processing has a vast range of applications across different fields. Below are some prominent areas where image processing techniques are effectively utilized.

Medical Imaging

In the field of medicine, image processing plays a crucial role in enhancing diagnostic capabilities. Techniques are employed in MRI (Magnetic Resonance Imaging), CT (Computed Tomography), and ultrasound imaging to improve image clarity and assist in disease detection. Advanced algorithms are utilized to analyze tumor shapes, detect anomalies, and monitor changes in patient conditions over time.

Remote Sensing

Remote sensing leverages image processing to extract information about the Earth and its environment. Satellites and drones capture vast amounts of data, which undergo processing to analyze land use, vegetation health, and environmental changes. Image classification algorithms facilitate the monitoring of natural resources, disaster management, and climate change assessments by transforming raw data into actionable information.

Security and Surveillance

The security industry has increasingly adopted image processing technologies for purposes such as surveillance, facial recognition, and license plate recognition. Automated systems analyze real-time footage, aiding in the identification of individuals and monitoring of environments for security threats. Furthermore, image processing contributes to the enhancement of video quality and detail extraction, making surveillance systems more effective.

Entertainment and Media

In the entertainment industry, image processing is essential in visual effects, computer-generated imagery (CGI), and animation. Films and video games leverage these techniques to create immersive experiences, often requiring complex image manipulation for realistic visuals. Additionally, platforms for photo editing and enhancement allow users to apply various filters and modifications to images, enhancing user creativity and expression.

Automotive Industry

Modern vehicles increasingly incorporate image processing for features such as lane detection, obstacle recognition, and parking assistance. Cameras and sensors capture environmental data that algorithms process to provide feedback to drivers or for autonomous driving systems. As automotive technology evolves, the reliance on sophisticated image processing techniques continues to grow.

Research and Development

Ongoing research in image processing explores new methods for improving image quality, data extraction, and usability across applications. Fields such as artificial intelligence, computer vision, and robotics heavily rely on advancements in image processing for developing innovative technologies. Researchers work on optimizing algorithms for real-time processing, expanding the boundaries of what image processing can achieve.

Real-world Examples

To illustrate the impact of image processing, several real-world applications and case studies are discussed below.

Medical Example

In oncology, advanced image processing techniques are employed to analyze MRI scans of patients suffering from cancer. By enhancing the contrast of tumor areas and applying segmentation algorithms, radiologists can more accurately identify and measure tumor growth. These methods have been shown to improve diagnosis and treatment planning for patients, significantly enhancing clinical outcomes.

Remote Sensing Example

A notable application of image processing in remote sensing involves monitoring deforestation rates in the Amazon Rainforest. Satellite images are processed using classification techniques to identify changes in land cover and assess the impact of logging activities. By utilizing image processing, researchers can provide critical insights into environmental changes and support policies aimed at conservation.

Surveillance Example

Facial recognition technology has been implemented by law enforcement agencies in various countries for crime detection and prevention. Image processing algorithms analyze video footage from public spaces to identify individuals from databases. This application enhances public safety while raising important ethical considerations regarding privacy and data use.

Automotive Example

The implementation of autonomous vehicles relies heavily on image processing technologies to function safely. Systems utilize cameras and sensors to capture real-time data about surroundings and road conditions. Image processing algorithms analyze this data, allowing vehicles to navigate, avoid obstacles, and follow traffic rules. This development represents a significant advancement in transportation technology, with ongoing research focused on improving the robustness of these systems.

Criticism or Limitations

Despite its advancements and applications, image processing is not without criticism and limitations. Variations in image quality, data loss, and ethical concerns present ongoing challenges.

Image Quality and Data Loss

Variabilities in lighting conditions, noise, and sensor quality can significantly impact the efficacy of image processing results. In medical imaging, for instance, poor image quality can hinder diagnosis and mislead clinical decisions. Additionally, data loss during compression or transmission may compromise the integrity of the information extracted from images.

Ethical Concerns

The use of image processing technologies, particularly in surveillance and facial recognition, raises important ethical questions surrounding privacy, consent, and bias. Concerns regarding the potential for misuse of images and data have prompted discussions on the need for regulations governing the implementation of such technologies. Furthermore, biases present in training data can lead to inequalities when algorithms are applied in real-world scenarios.

Dependence on Technology

The increasing reliance on automated image processing systems may diminish the role of human oversight in critical applications such as medicine and security. While algorithms can enhance efficiency, they may also introduce new types of errors that are not immediately apparent. The balance between automation and human involvement remains a key consideration.

Processing Speed and Real-time Limitations

Many image processing applications require real-time processing capabilities, which poses a challenge regarding computational power and efficiency. As imaging data continues to grow in size and complexity, the demand for faster algorithms and processing units gains significance. Improvements in hardware and optimization of algorithms are critical for meeting the needs of real-time applications.

References

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-== Image Processing ==
+'''Image Processing''' is a method of manipulating digital images through the use of algorithms and software techniques. The primary goal of image processing is to enhance the quality of an image or to extract useful information from it. This field encompasses a range of techniques that can be applied in various domains, including computer vision, medical imaging, remote sensing, and more. As technology progresses, image processing techniques are becoming increasingly sophisticated and widely applied in industries such as entertainment, healthcare, and security.
-Image processing is a method of manipulating and analyzing digital images using various algorithms and techniques. It has applications across numerous fields, including computer vision, machine learning, medical imaging, and more. The goal of image processing is to enhance image quality, extract useful information, or perform analysis and transformations on the image data.
+== Background or History ==
+The origins of image processing can be traced back to the early 1960s when researchers began to experiment with the manipulation and analysis of images through computers. Early methods focused primarily on simple geometric transformations and basic enhancements. The introduction of the digital computer played a significant role in the evolution of this field, allowing for more complex algorithms to be developed.
-== Introduction ==
+=== 1960s to 1970s ===
+During the 1960s, pioneers like Lawrence Roberts and others began developing mathematical models for image analysis, laying the groundwork for future advancements in image processing. At this time, systems were primarily used in research laboratories and were characterized by their limited capabilities.
-Image processing comprises a broad spectrum of techniques and approaches, which can be divided into two main categories: **analog image processing** and **digital image processing**. Analog image processing involves direct manipulation of image data, such as adjusting contrast or applying filters to photographic film. Digital image processing, on the other hand, leverages computer algorithms to process and analyze image data represented in digital format.
+The 1970s saw the advent of more sophisticated techniques, including edge detection, filtering, and pattern recognition. Researchers such as David Marr were instrumental in formulating theories about the human visual system, which influenced the development of algorithms for image processing. Notably, the development of computer graphics also bolstered the field, providing new avenues for visual representation.
-Digital image processing has gained prominence due to the exponential growth of digital content creation and the need for effective analysis and manipulation of images. The increasing availability of powerful computational resources and sophisticated software has made it possible to develop advanced image processing techniques that drive innovation in various sectors, from art and entertainment to healthcare and scientific research.
+=== 1980s to 1990s ===
+The 1980s marked a significant shift in image processing with the introduction of digital cameras and the increased availability of digital image data. This era also saw the emergence of specialized software such as Adobe Photoshop, which put image processing tools into the hands of artists and general users. Algorithms that utilized techniques like Fourier transforms became commonplace, facilitating advancements in frequency domain analysis.
-== History ==
+By the 1990s, the field matured, leveraging the rise of computers and digital imaging technologies. The introduction of new image formats and standards allowed for greater interoperability and quality in image sharing. Image processing began being integrated into various applications including telecommunications, medical imaging, and automated inspection systems.
-The roots of image processing can be traced back to the 1960s when scientists began to investigate computer-based techniques for manipulating images. Early efforts in image processing primarily involved simple operations like histogram equalization and filtering.
+=== 2000s and Beyond ===
+With the advent of the 21st century, image processing entered a new era characterized by the explosion of digital content and the availability of powerful computational resources. Machine learning and artificial intelligence began to revolutionize image processing, enabling sophisticated applications such as facial recognition and image classification. Technologies like deep learning and convolutional neural networks (CNNs) paved the way for remarkable advancements in automated image analysis.
-In the 1970s, the advent of digital computers revolutionized the capability of image processing. Research began to focus on more complex algorithms, including edge detection and image segmentation. Notable contributions during this period include the development of the Fast Fourier Transform (FFT) algorithm, which allowed for efficient frequency domain analysis of images.
+In contemporary use, image processing techniques are an integral part of smartphone applications, social media platforms, and autonomous systems. The integration of image processing with machine learning continues to expand its capabilities, leading to new developments in augmented reality, 3D imaging, and beyond.
-The 1980s and 1990s saw significant advances in image processing techniques, fueled by the increasing use of computers and digital cameras. This era marked the development of various algorithms for image compression, such as JPEG, which made it feasible to store and transmit large volumes of image data.
+== Architecture and Design ==
+Image processing systems primarily consist of hardware and software components that work together to perform various tasks. The architecture often varies based on the specific application and the complexity of the processing tasks.
-With the rise of the Internet and digital photography in the late 1990s and early 2000s, image processing surged in popularity. Software applications such as Adobe Photoshop became ubiquitous, providing users with powerful tools to edit and manipulate images. Academic research in the field also expanded, leading to the establishment of image processing as a distinct scientific discipline.
+=== Hardware Components ===
+The hardware utilized in image processing includes processors, cameras, sensors, and memory storage systems. High-performance processors, such as Graphics Processing Units (GPUs), are common in modern image processing applications due to their ability to handle parallel processing efficiently. Additionally, specialized hardware like Field Programmable Gate Arrays (FPGAs) may be used for real-time image processing tasks.
-== Design or Architecture ==
+=== Software Components ===
+At the software level, image processing relies on a variety of programming languages and libraries. Languages such as Python, C++, and MATLAB are frequently utilized because of their robust libraries and frameworks like OpenCV and TensorFlow, which provide tools to implement complex algorithms. Image processing software typically includes functionalities like image filtering, segmentation, feature extraction, and compression techniques.
-The design and architecture of image processing systems vary depending on the scale and application. However, most image processing systems follow a similar conceptual framework, which includes the following stages:
+=== Algorithms and Techniques ===
+Central to image processing are various algorithms that are used to enhance, modify, and analyze images. Common techniques include:
+* **Image enhancement**: Techniques such as histogram equalization and contrast stretching improve the visual quality of an image.
+* **Image restoration**: Algorithms that remove noise or correct features for a clearer representation of the original scene, such as deblurring techniques.
+* **Image segmentation**: Methods that partition an image into sections or objects, which can facilitate later analysis and understanding.
+* **Feature extraction**: Techniques that identify and isolate significant features or patterns within an image, often used in machine learning tasks.
-. **Image Acquisition**: The process begins with capturing the image, which can be done using various devices such as cameras, scanners, or sensors. The quality of the acquired image plays a pivotal role in subsequent processing stages.
+These algorithms serve as the backbone of image processing applications and can be combined in various ways to achieve desired outcomes.
-. **Preprocessing**: This stage involves techniques to enhance image quality, remove noise, and correct distortions. Common preprocessing techniques include filtering, histogram equalization, and geometric transformations.
+== Implementation and Applications ==
+Image processing has a vast range of applications across different fields. Below are some prominent areas where image processing techniques are effectively utilized.
-. **Feature Extraction**: After preprocessing, relevant features are extracted from the image. This may include identifying edges, corners, textures, or other characteristics that are essential for further analysis.
+=== Medical Imaging ===
+In the field of medicine, image processing plays a crucial role in enhancing diagnostic capabilities. Techniques are employed in MRI (Magnetic Resonance Imaging), CT (Computed Tomography), and ultrasound imaging to improve image clarity and assist in disease detection. Advanced algorithms are utilized to analyze tumor shapes, detect anomalies, and monitor changes in patient conditions over time.
-. **Image Segmentation**: Segmentation involves partitioning an image into meaningful regions or segments. This enables the identification of objects, boundaries, and relevant areas within the image.
+=== Remote Sensing ===
+Remote sensing leverages image processing to extract information about the Earth and its environment. Satellites and drones capture vast amounts of data, which undergo processing to analyze land use, vegetation health, and environmental changes. Image classification algorithms facilitate the monitoring of natural resources, disaster management, and climate change assessments by transforming raw data into actionable information.
-. **Image Analysis**: In this stage, algorithms are applied to analyze segmented regions and derive information, such as object recognition and classification, shape analysis, or geometric transformations.
+=== Security and Surveillance ===
+The security industry has increasingly adopted image processing technologies for purposes such as surveillance, facial recognition, and license plate recognition. Automated systems analyze real-time footage, aiding in the identification of individuals and monitoring of environments for security threats. Furthermore, image processing contributes to the enhancement of video quality and detail extraction, making surveillance systems more effective.
-. **Post-processing**: The final stage involves refining the processed output, which may include enhancing visualization or displaying the image in different formats. This stage ensures that the processed results meet the specific requirements of the intended application.
+=== Entertainment and Media ===
+In the entertainment industry, image processing is essential in visual effects, computer-generated imagery (CGI), and animation. Films and video games leverage these techniques to create immersive experiences, often requiring complex image manipulation for realistic visuals. Additionally, platforms for photo editing and enhancement allow users to apply various filters and modifications to images, enhancing user creativity and expression.
-The overall architecture of an image processing system can be modular, allowing for flexibility in selecting different algorithms and techniques at different stages based on the specific needs of the application.
+=== Automotive Industry ===
+Modern vehicles increasingly incorporate image processing for features such as lane detection, obstacle recognition, and parking assistance. Cameras and sensors capture environmental data that algorithms process to provide feedback to drivers or for autonomous driving systems. As automotive technology evolves, the reliance on sophisticated image processing techniques continues to grow.
-== Usage and Implementation ==
+=== Research and Development ===
+Ongoing research in image processing explores new methods for improving image quality, data extraction, and usability across applications. Fields such as artificial intelligence, computer vision, and robotics heavily rely on advancements in image processing for developing innovative technologies. Researchers work on optimizing algorithms for real-time processing, expanding the boundaries of what image processing can achieve.
-Image processing is employed in a myriad of applications across various domains:
-===  Medical Imaging ===
-In the medical field, image processing plays a crucial role in enhancing and analyzing images obtained from imaging modalities such as X-rays, CT scans, MRIs, and ultrasounds. Techniques such as noise reduction, edge enhancement, and 3D reconstruction are widely used to support diagnosis and treatment planning. For instance, image segmentation algorithms help detect tumors or other anomalies by isolating interested regions from the surrounding tissues.
-===  Computer Vision ===
-Computer vision, a subfield of artificial intelligence, relies heavily on image processing techniques. Applications include autonomous vehicles, facial recognition, object detection, and gesture recognition. Techniques such as convolutional neural networks (CNNs) leverage feature extraction and classification to enable machines to understand visual information from their environment.
-===  Remote Sensing ===
-Image processing is a critical component of remote sensing technologies used to gather information about the Earth’s surface from satellites or aerial imagery. Applications include land-use classification, vegetation analysis, and environmental monitoring. Techniques such as color composite analysis and image fusion are utilized to generate meaningful outputs from multi-spectral and multi-temporal data.
-===  Entertainment Industry ===
-The entertainment and gaming industries utilize image processing for various purposes, including visual effects, motion capture, and augmented reality (AR). Image processing techniques allow for creating realistic rendering, real-time video editing, and interactive experiences.
-===  Forensic Science ===
-In forensic science, image processing is indispensable for enhancing evidence captured in low visibility conditions. Techniques such as image restoration and enhancement can improve visual quality and resolve details of evidence, aiding investigations and court presentations.
 == Real-world Examples ==
+To illustrate the impact of image processing, several real-world applications and case studies are discussed below.
-Many real-world applications of image processing underscore its significance in modern society:
+=== Medical Example ===
+In oncology, advanced image processing techniques are employed to analyze MRI scans of patients suffering from cancer. By enhancing the contrast of tumor areas and applying segmentation algorithms, radiologists can more accurately identify and measure tumor growth. These methods have been shown to improve diagnosis and treatment planning for patients, significantly enhancing clinical outcomes.
-===  Digital Cameras and Smartphones ===
-Modern digital cameras and smartphones utilize advanced image processing algorithms to enhance photo quality in real-time. Features such as HDR (high dynamic range), image stabilization, and noise reduction apply various processing techniques to optimize the final output automatically.
-===  Face Detection Software ===
-Face detection is a common application of image processing in security systems and social media platforms. Technologies like Haar cascades and deep learning algorithms are utilized to detect human faces within images, enabling functionalities such as tagging, filtering, and access control.
-===  Automated Medical Image Analysis ===
-Automated systems employing image processing algorithms assist radiologists in interpreting medical images. These systems can quickly identify potential health issues, reducing the time needed for analysis and increasing diagnostic accuracy.
-===  Satellite Imagery Analysis ===
-Satellite imagery processing is utilized in environmental monitoring, disaster management, and urban planning. Image processing techniques help classify land cover, monitor changes over time, and detect natural disasters through detailed analysis of multi-spectral data.
-===  Industrial Automation ===
-Image processing systems are employed in manufacturing processes for quality control. Visual inspection systems use cameras and image processing algorithms to identify defects, measure dimensions, and ensure product consistency with minimal human intervention.
-== Criticism and Controversies ==
-While image processing has brought numerous advancements, it is not devoid of criticism and controversy:
-===  Privacy Concerns ===
+=== Remote Sensing Example ===
-The use of image processing technologies, particularly in surveillance systems, raises significant privacy issues. Facial recognition technology, in particular, has come under scrutiny for the potential to infringe on personal privacy rights and enable mass surveillance.
+A notable application of image processing in remote sensing involves monitoring deforestation rates in the Amazon Rainforest. Satellite images are processed using classification techniques to identify changes in land cover and assess the impact of logging activities. By utilizing image processing, researchers can provide critical insights into environmental changes and support policies aimed at conservation.
-===  Misuse of Technology ===
+=== Surveillance Example ===
-Image processing techniques can be misused to create deepfakes or manipulate images to mislead the public or damage reputations. The ability to alter images convincingly poses ethical challenges and necessitates the development of policies to manage technology's impact on society.
+Facial recognition technology has been implemented by law enforcement agencies in various countries for crime detection and prevention. Image processing algorithms analyze video footage from public spaces to identify individuals from databases. This application enhances public safety while raising important ethical considerations regarding privacy and data use.
-===  Algorithmic Bias ===
+=== Automotive Example ===
-The application of image processing in areas like facial recognition can result in algorithmic bias, where certain demographic groups are misrepresented or misidentified. This raises concerns regarding fairness and accountability when deploying such technologies, especially in law enforcement and security measures.
+The implementation of autonomous vehicles relies heavily on image processing technologies to function safely. Systems utilize cameras and sensors to capture real-time data about surroundings and road conditions. Image processing algorithms analyze this data, allowing vehicles to navigate, avoid obstacles, and follow traffic rules. This development represents a significant advancement in transportation technology, with ongoing research focused on improving the robustness of these systems.
-== Influence and Impact ==
+== Criticism or Limitations ==
+Despite its advancements and applications, image processing is not without criticism and limitations. Variations in image quality, data loss, and ethical concerns present ongoing challenges.
-The impact of image processing on society is profound, influencing various industries, scientific research, and everyday life. Its advancements foster innovation in technology, medicine, and entertainment, significantly enhancing human capability and understanding of visual information.
+=== Image Quality and Data Loss ===
+Variabilities in lighting conditions, noise, and sensor quality can significantly impact the efficacy of image processing results. In medical imaging, for instance, poor image quality can hinder diagnosis and mislead clinical decisions. Additionally, data loss during compression or transmission may compromise the integrity of the information extracted from images.
-===  Technological Advances ===
+=== Ethical Concerns ===
-Continued innovations in image processing contribute to advancements in related fields such as artificial intelligence and machine learning. The integration of deep learning techniques has transformed how images are processed, leading to breakthroughs in object detection and scene understanding.
+The use of image processing technologies, particularly in surveillance and facial recognition, raises important ethical questions surrounding privacy, consent, and bias. Concerns regarding the potential for misuse of images and data have prompted discussions on the need for regulations governing the implementation of such technologies. Furthermore, biases present in training data can lead to inequalities when algorithms are applied in real-world scenarios.
-===  Economic Growth ===
+=== Dependence on Technology ===
-The production and consumption of visual content have become integral to the global economy, influencing advertising, media, and entertainment sectors. Image processing technologies have facilitated this growth by enabling the creation, sharing, and analysis of vast quantities of visual data.
+The increasing reliance on automated image processing systems may diminish the role of human oversight in critical applications such as medicine and security. While algorithms can enhance efficiency, they may also introduce new types of errors that are not immediately apparent. The balance between automation and human involvement remains a key consideration.
-===  Education and Research ===
+=== Processing Speed and Real-time Limitations ===
-In academia, image processing plays a vital role in research and education, impacting fields such as computer science, engineering, biology, and social sciences. Image processing routines are commonly used in research projects and educational curricula to teach students essential computational and analytical skills.
+Many image processing applications require real-time processing capabilities, which poses a challenge regarding computational power and efficiency. As imaging data continues to grow in size and complexity, the demand for faster algorithms and processing units gains significance. Improvements in hardware and optimization of algorithms are critical for meeting the needs of real-time applications.
 == See also ==
 * [[Computer Vision]]
-* [[Digital Signal Processing]]
 * [[Machine Learning]]
-* [[Medical Imaging]]
+* [[Digital Image Processing]]
-* [[Image Compression]]
+* [[Optical Character Recognition]]
-* [[Histogram Equalization]]
+* [[Computer Graphics]]
+* [[Image Analysis]]
 == References ==
-* [https://www.itu.int/en/journal/002/Pages/image-processing.aspx International Telecommunication Union - Image Processing Overview]
+* [https://www.imageprocessing.org/ International Society for Image Processing]
-* [https://www.sciencedirect.com/topics/computer-science/image-processing-technology Scientific Research on Image Processing Technologies]
+* [https://www.computer.org/ Institute of Electrical and Electronics Engineers (IEEE)]
-* [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442258/ Medical Image Processing - An Overview]
+* [https://www.opencv.org/ OpenCV – Open Source Computer Vision Library]
-* [https://ieeexplore.ieee.org/document/8437639 Computer Vision and Image Processing: Methods and Applications]
+* [https://www.mathworks.com/products/matlab.html MATLAB – The Language of Technical Computing]
-* [https://www.adobe.com/products/photoshop.html Adobe Photoshop - Image Processing Software]
+* [https://www.adobe.com/products/photoshop.html Adobe Photoshop – Image Editing Software]
-* [https://www.wiley.com/en-us/Image+Processing+-+Fundamentals+and+Applications-p-9781119638557 Wiley - Image Processing Book]
 [[Category:Image processing]]
-[[Category:Computer vision]]
+[[Category:Computer science]]
-[[Category:Signal processing]]
+[[Category:Digital media]]