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Introductory Chapter: On Digital Image Processing

Written By

Muhammad Sarfraz

Published: 13 May 2020

DOI: 10.5772/intechopen.92060

From the Edited Volume

Digital Imaging

Edited by Muhammad Sarfraz

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1. Introduction

An image would be called as an analog image if its pictorial representation can be represented in analog wave formats, whereas an image would be called as a digital image if its pictorial representation can be represented or stored in the data in digital form. Similarly, field of image processing can be categorized into digital image processing and analog image processing. Digital image processing (or digital imaging), in the area of computer science today, is defined as processing digital images through some algorithms using digital computers, whereas, analog image processing is any image processing task that can be conducted on two-dimensional analog signals by analog means [1, 2].

After the invention of digital computers, digital image processing took various advantages over analog image processing. A broad range of techniques and methods, in the form of a variety of algorithms, came into existence. One can find a rich literature toady which can be applied to the input image data to solve various problems. These problems may include converting images into digital data, calibration, removing the build-up of noise and distortion during processing, etc. Since images are defined over two dimensions (and perhaps more) digital image processing may be modeled in the form of multidimensional systems. Digital image processing has evolved rapidly with the development of computers, mathematics, and the real-life demand for a variety of applications in wide range of areas [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30].

In the current age and time, digital imaging is used widely in various real-life applications. There is a number of potential digital imaging applications that include different areas such as environment, industry, medical science, agriculture, military, film, television, photography, robotics, remote sensing, medical diagnosis, reconnaissance, architectural and engineering design, art, crime prevention, geographical information systems, communication, intellectual property, retail catalogs, nudity-detection, face finding, industrial applications, and others. The increasing trends, needs, and applications of imaging make it more difficult to process images for desired objectives. This leads to the idea of capturing, storing, finding, retrieving, analyzing, and using images in everyday life under the computing environment. Being a computer-based technology, digital imaging carries out automatic processing, manipulation, and interpretation of visual information. It plays a significant and important role in various aspects of real life. It is also highly useful in many areas, disciplines and fields of art, and science and technology. This chapter is specifically dedicated to digital imaging history, methodologies, tasks, software, and applications [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57].


2. History

One of the earliest applications of digital image, in the early 1920s, can be seen in the newspaper industry. It was about the pictures that were sent by submarine cable between London and New York. The Bart lane cable picture transmission system reduced the amount of time tremendously weeks to hours across the Atlantic. As the field of digital image processing developed along with the development of the modern digital computers in 1950s, various techniques, methods, and technologies of digital image processing were developed in the 1960s at various places. Some of those places can be named as Bell Laboratories, the Jet Propulsion Laboratory, Massachusetts Institute of Technology, and University of Maryland. Together with them, there were also some other research facilities for satellite imagery, medical imaging, wire-photo standards conversion, photograph enhancement, videophone, and character recognition [3]. In the early days, image processing was mainly meant for improving the image quality in general. Very basic and commonly used techniques in image processing included enhancement, restoration, encoding, and compression of images.

American Jet Propulsion Laboratory (JPL) happened to be the first successful application in 1960s. Using this, in 1964, Space Detector Ranger 7 sent thousands of lunar photos. They mainly used image processing techniques like geometric correction, gradation transformation, and noise removal on the sent lunar photos. It was a big success story to have the successful computerized mapping of the moon’s surface. The success kept progressing so much so that spacecraft sent nearly 100,000 photos that were processed with more complex imaging functionalities. It helped to obtain the topographic map, color map, and panoramic mosaic of the moon. This resulted in extraordinary achievements and happened to be landmark basis of history for human landing on the moon [4].

This is true that, due to computing machines of 1960s and earlier, the processing cost was fairly high. With the passage of time, in the 1970s, however, things changed relatively with faster digital image processing and cheaper computing equipment. Slowly and gradually afterwards, processing power kept increasing together with lower cost machines which resulted in images to be processed faster and faster. So much so, various complex problems like television standards conversion were managed in real time. In the years 2000s and after, the general-purpose computing equipment became much faster. Various developments in the technological world led to dedicated and special purpose hardware and equipment. Today, digital image processing has turned to a vital computing discipline which is playing a significant role to solve various real life problems in real time.


3. Methodology and tasks

Image processing is a very important area in today’s science and engineering. The processing of digital images can be divided into various classes including image enhancement, image restoration, image analysis, and image compression. Imaging provides methodology to perform some kind of operations on input images. The output is obtained in terms of enhanced images, or some desired information, or some required features. For the sake of smooth workflow, it is important to first capture images and then to process them afterwards. Image processing techniques work on digital images with computing algorithms. Various steps and phases are needed to work on the images. For example, first, one can convert signals from an image sensor into digital images. After that, we can improve clarity and remove noise. Next, steps may be extracting the size, scaling, or desired objects in a scene. Then, images can be prepared for display. Lastly but not finally, compression of images is a very important phenomenon as it is needed for communication across busy networks.

There are various other phases and tasks which need attention depending on nature of applications. These include morphological processing, segmentation, enhancement, object recognition, and color image processing. Digital image processing involves much more sophisticated and useful computer algorithms. Most of the times, it is based on classification, feature extraction, multi-scale signal analysis, pattern recognition, and projection. Some of the popular techniques that can be used in digital image processing include anisotropic diffusion, Hidden Markov models, image editing, image restoration, independent component analysis, linear filtering, neural networks, partial differential equations, pixelization, point feature matching, principal components analysis, self-organizing maps, and wavelets.


4. Software and applications

There are a variety of software that can be used for image processing. For example, Matlab has many tools for image processing; it also facilitates to develop graphical user interfaces (GUI). ImageJ can be utilized for simple things, whereas Amira can be used for complex tasks. In case of medical applications, eFilm is one of the useful tools.

Applications of image processing range from medicine to entertainment and much more. Some of the important applications of image processing in the field of science, engineering, and technology include image sharpening and restoration, remote sensing, feature extraction, face detection, forecasting, optical character recognition, biometrics, medical imaging, optical sorting, augmented reality, virtual reality, video processing, microscope imaging, license plate recognition, lane departure caution, transportation, parking, transmission and encoding, machine vision, robotics, color processing, signature recognition, iris recognition, face recognition, forensics, automobile detection, fault detection, pattern recognition, military applications, and others. Following subsection has been dedicated to an application of license plate recognition (LPR) with systematic methodologies.

4.1 License plate recognition

Here is an example of different tasks and phases for a system to recognize license plates from the front and rear of the vehicle [58, 59, 60]. Input to the system is an image sequence acquired by a digital camera that consists of a license plate and its output is the recognition of characters on the license plate. The system consists of the standard four main modules in an LPR system which includes image acquisition, license plate extraction, license plate segmentation, and license plate recognition. The structure of the system is shown in Figure 1. The first task acquires the selected portion of the image (i.e., the portion which contains a license plate). The second task extracts the region that contains the license plate. The third task isolates the characters, consisting of letters and numerals, depending on the targeted License Plates. The last task identifies or recognizes the segmented characters.

Figure 1.

Structure of the proposed system.

Image acquisition: This is the first phase in an LPR system. This phase deals with acquiring an image by an acquisition method. In the LPR system, we need to use a high resolution digital camera to acquire the input image. The input image can be taken for example 640 × 480 pixels.

License plate extraction: License plate extraction is a key step in an LPR system, which influences the accuracy of the system significantly. This phase extracts the region of interest, i.e., the license plate, from the acquired image. The proposed approach involves four steps including vertical edge detection, size-and-shape filtering, vertical edge matching, and finding B/W (Black/White) ratio.

License plate segmentation: License plate segmentation takes the region of interest and attempts to divide it into individual characters. To ease the process of detecting the characters, the extracted plate is divided into independent images, each containing one isolated character with letters and numerals depending on the structure of the license plate. It is proposed to have segmentation using two methods: Pixel Count and Horizontal and Vertical Projection.

License plate recognition: The last phase in LPR system is to recognize the isolated characters. After splitting the extracted license plate into six images, the character in each image can be identified. There are many methods to recognize isolated characters; we suggest using Syntactic approach and Neural network approach.


5. Conclusion

With the advent of fast and cheap machines, digital image processing has become a very highly demanded field of study and practice. It provides solutions to various real-life applications in an economical way. Various techniques have been developed to build intelligent systems; many of them are in progress at various facilities internationally. This chapter has provided some introductory notes on image processing, its brief history, methodologies, tasks, software, and applications. It will help to kick start the community interested to have some knowhow on the image processing subject. The future of digital image processing has a high probability to contribute toward the build of smart and intelligent world in terms of health, education, defense, traffic, homes, offices, cities, etc.


  1. 1. Chakravorty P. What is a signal? [lecture notes]. IEEE Signal Processing Magazine. 2018;35(5):175-177. DOI: 10.1109/MSP.2018.2832195
  2. 2. Gonzalez R. Digital image processing. New York, NY: Pearson; 2018. ISBN: 978-0-13-335672-4. OCLC 966609831
  3. 3. Rosenfeld A. Picture Processing by Computer. New York: Academic Press; 1969
  4. 4. Gonzalez RC, Woods RE. Digital Image Processing. 4th ed. New York, NY, USA: Pearson; 2018. ISBN-13: 978-0133356724
  5. 5. Williams JB. The Electronics Revolution: Inventing the Future. Chichester, UK: Springer; 2017. pp. 245-248. ISBN: 9783319490885
  6. 6. 1960: Metal Oxide Semiconductor (MOS) Transistor Demonstrated. The Silicon Engine. Computer History Museum. Archived from the original on 3 October 2019. [Accessed: 31 August 2019]
  7. 7. Janesick JR. Scientific Charge-Coupled Devices. Bellingham, Washington, USA: SPIE Press; 2001. pp. 3-4. ISBN: 978-0-8194-3698-6
  8. 8. Boyle WS, Smith GE. Charge coupled semiconductor devices. Bell System Technical Journal. 1970;49(4):587-593. DOI: 10.1002/j.1538-7305.1970.tb01790.x
  9. 9. Fossum, Eric R. Active pixel sensors: Are CCDS dinosaurs?. In: Blouke, Morley M, editors. Charge-Coupled Devices and Solid State Optical Sensors III. Proceedings of the SPIE, 1900. Bellingham, Washington, USA: SPIE Press; 1993. pp. 2-14
  10. 10. Fossum ER. Active Pixel Sensors. Semantic Scholar. Archived (PDF) from the original on 9 March 2019. 2007 [Accessed: 08 October 2019]
  11. 11. Matsumoto, Kazuya, et al. A new MOS phototransistor operating in a non-destructive readout mode. Japanese Journal of Applied Physics. 1985;24(5A):L323. Bibcode:1985JaJAP..24L.323M. DOI: 10.1143/JJAP.24.L323
  12. 12. Fossum ER, Hondongwa DB. A review of the pinned photodiode for CCD and CMOS image sensors. IEEE Journal of the Electron Devices Society. 2014;2(3):33-43. DOI: 10.1109/JEDS.2014.2306412
  13. 13. CMOS Image Sensor Sales Stay on Record-Breaking Pace. IC Insights. 8 May 2018. Archived from the original on 21 June 2019 [Accessed: 06 October 2019]
  14. 14. Ahmed N. How I came up with the discrete cosine transform. Digital Signal Processing. 1991;1(1):4-5. DOI: 10.1016/1051-2004(91)90086-Z
  15. 15. T.81 – Digital Compression and Coding of Continuous-Tone Still Images – Requirements and Guidelines. CCITT. September 1992. Archived (PDF) from the original on 17 July 2019 [Accessed: 12 July 2019]
  16. 16. The JPEG Image Format Explained. BT Group. 31 May 2018. Archived from the original on 5 August 2019 [Accessed: 05 August 2019]
  17. 17. What Is a JPEG? The Invisible Object You See Every Day. The Atlantic. 24 September 2013. Archived from the original on 9 October 2019 [Accessed: 13 September 2019]
  18. 18. Baraniuk C. Copy protections could come to JPEGs. BBC News. BBC. Archived from the original on 9 October 2019. 2015 [Accessed: 13 September 2019]
  19. 19. Grant DA, Gowar J. Power MOSFETS: Theory and Applications. New York, USA: John Wiley & Sons; 1989. p. 1. ISBN: 9780471828679. The metal-oxide-semiconductor field-effect transistor (MOSFET) is the most commonly used active device in the very large-scale integration of digital integrated circuits (VLSI). During the 1970s these components revolutionized electronic signal processing, control systems and computers
  20. 20. Shirriff K. The Surprising Story of the First Microprocessors. IEEE Spectrum. Institute of Electrical and Electronics Engineers. Archived from the original on 13 October 2019. 2016 [Accessed: 13 October 2019]
  21. 21. 1979: Single Chip Digital Signal Processor Introduced. The Silicon Engine. Computer History Museum. Archived from the original on 3 October 2019 [Accessed: 14 October 2019]
  22. 22. Taranovich S. 30 Years of DSP: From a Child's Toy to 4G and Beyond. EDN. Archived from the original on 14 October 2019. 2012 [Accessed: 14 October 2019]
  23. 23. Stanković RS, Astola JT. Reminiscences of the Early Work in DCT: Interview with K. R. Rao (PDF). Reprints from the Early Days of Information Sciences. 60. Archived (PDF) from the original on 13 October 2019. 2012 [Accessed: 13 October 2019]
  24. 24. Space Technology Hall of Fame: Inducted Technologies/1994. Space Foundation. Archived from the original on 4 July 2011. 1994 [Accessed: 07 January 2010]
  25. 25. Zhang MZ, Livingston AR, Asari VK. A high performance architecture for implementation of 2-D convolution with quadrant symmetric kernels. International Journal of Computers and Applications. 2008;30(4):298-308. DOI: 10.1080/1206212x.2008.11441909
  26. 26. Gonzalez R. Digital Image Processing. 3rd ed. New York, USA: Pearson Hall; 2008. ISBN: 9780131687288
  27. 27. House K. Affine Transformations (PDF). Clemson. Foundations of Physically Based Modeling & Animation. A K Peters/CRC Press. ISBN: 9781482234602. Archived (PDF) from the original on 30 August 2017. 2016 [Accessed: 26 March 2019]
  28. 28. A Brief, Early History of Computer Graphics in Film Archived 17 July 2012 at the Wayback Machine, Larry Yaeger, 16 August 2002 (last update) [Accessed: 24 March 2010]
  29. 29. Digital Image Processing, From Wikipedia, the Free Encyclopedia [Accessed: 05 March 2020]
  30. 30. Solomon CJ, Breckon TP. Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Chichester, UK: Wiley-Blackwell; 2010
  31. 31. Burger W, Burge MJ. Digital Image Processing: An Algorithmic Approach Using Java. London, UK: Springer; 2007. ISBN: 978-1-84628-379-6
  32. 32. Fisher R, Dawson-Howe K, Fitzgibbon A, Robertson C, Trucco E. Dictionary of Computer Vision and Image Processing. Chichester, UK: John Wiley; 2005. ISBN: 978-0-470-01526-1
  33. 33. Gonzalez RC, Woods RE, Eddins SL. Digital Image Processing Using MATLAB. India: Pearson Education; 2004. ISBN: 978-81-7758 -898-9
  34. 34. Morris T. Computer Vision and Image Processing. New York, USA: Palgrave Macmillan; 2004. ISBN: 978-0-333-99451-1
  35. 35. Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision. Pacific Grove, CA, USA: PWS Publishing; 1999. ISBN: 978-0-534-95393-5
  36. 36. Gonzalez RC. Digital Image Processing. New York, USA: Prentice Hall; 2008. ISBN: 9780131687288
  37. 37. Ebad Banissi, Anna Ursyn, Autilia Vitiello, Fatma Bouali, Gilles Venturin, Hanane Azzag, et al. Information Visualization - Biomedical Visualization, Visualisation on Built and Rural Environments & Geometric Modelling and Imaging. USA: IEEE Computer Society. 2019
  38. 38. Banissi E, Francese R, McK Bannatyne MW, Wyeld TG, Sarfraz M, Pires JM, et al, editors. Information Visualization - Biomedical Visualization, Visualisation on Built and Rural Environments & Geometric Modellinpg and Imaging. USA: IEEE Computer Society; 2018
  39. 39. Banissi E, McK Bannatyne MW, Bouali F, Datia NMS, Grinstein G, et al, editors. Information Visualization - Biomedical Visualization, Visualisation on Built and Rural Environments & Geometric Modelling and Imaging, IEETe 2017. USA: IEEE Computer Society; 2017
  40. 40. Banissi E, Sarfraz M, Zeroul A, Fakir M, editors. Computer Graphics, Imaging & Visualization. New Horizons, USA: IEEE; 2017
  41. 41. Banissi E, Pires JM, McK MW, Bannatyne UC, Grinstein G, Huang T, et al, editors. Information Visualization - Biomedical Visualization, Visualisation on Built and Rural Environments & Geometric Modelling and Imaging. USA: IEEE Computer Society; 2016
  42. 42. Banissi E, Sarfraz M, Fakir M, editors. Computer Graphics, Imaging & Visualization: New Techniques and Trends. USA: IEEE; 2016
  43. 43. Banissi E, Sarfraz M, editors. Computer Graphics, Imaging & Visualization: New Techniques and Trends. USA: IEEE; 2015
  44. 44. Banissi E, McK Bannatyne MW, Marchese FT, Wyeld TG, Sarfraz M, Ursyn A, et al, editors. Information Visualisation - Computer Graphics, Imaging and Visualisation: Biomedical Visualization, Visualisation on Built and Rural Environments & Geometric Modelling and Imaging. USA: IEEE Computer Society; 2015
  45. 45. Sarfraz M. Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies. Hershey, PA: IGI Global; 2014. pp. 1-312
  46. 46. Banissi E, Sarfraz M. Computer Graphics, Imaging & Visualization: New Techniques and Trends. USA: IEEE; 2014
  47. 47. Banissi E, McK Bannatyne MW, Marchese FT, Sarfraz M, Ursyn A, Venturini G, et al. Information Visualization: Visualisation, BioMedical Visualization, Visualisation on Built and Rural Environments & Geometric Modelling and Imaging. USA: IEEE; 2014
  48. 48. Sarfraz M. Interactive Curve Modeling with Applications to Computer Graphics, Vision and Image Processing. London, UK: Springer-Verlag; 2008
  49. 49. Saad A, Avineri E, Dahal K, Sarfraz M, Roy R. Soft Computing in Industrial Applications: Recent and Emerging Methods and Techniques, Series: Advances in Soft Computing, Vol. 39. London, UK: Springer- Verlag; 2007
  50. 50. Sarfraz M. Computer Aided Intelligent Recognition Techniques and Applications. Chichester, UK: John Wiley and Sons; 2005
  51. 51. Khalloufi R, El Ayachi R, Biniz M, Fakir M, Sarfraz M. An approach of documents indexing using summarization. In: Sarfraz M, editor. Critical Approaches to Information Retrieval Research. Hershey, PA: IGI Global; 2020. pp. 78-86
  52. 52. Taifi K, Taifi N, Fakir M, Safi S, Sarfraz M. Mammogram classification using nonsubsampled contourlet transform and gray-level co-occurrence matrix. In: Sarfraz M, editor. Critical Approaches to Information Retrieval Research. Hershey, PA: IGI Global; 2020. pp. 239-255
  53. 53. Fakir M, Hicham H, Chabi M, Sarfraz M. Classification of eye based on fuzzy logic. International Journal of Cognitive Informatics and Natural Intelligence. 2020;14(4):1-20
  54. 54. Ebrahimi AR, Loghmani GB, Sarfraz M. Capturing outlines of generic shapes with cubic B’ezier curves using the Nelder–Mead simplex method. Iranian Journal of Numerical Analysis and Optimization. 2019;9(2):103-121. DOI: 10.22067/ijnao.v9i2.70045
  55. 55. Zulkifli NAB, Karim SAA, Shafie AB, Sarfraz M. Rational bicubic ball for image interpolation. Journal of Physics: Conference Series. 2019;1366(1):1-11. DOI: 10.1088/1742-6596/1366/1/012097
  56. 56. Zulkifli NAB, Karim SAA, Shafie AB, Sarfraz M, Gaffar A, Nisar KS. Image interpolation using rational bi-cubic ball. Mathematics. 2019;7(11):1-18. DOI: 10.3390/math7111045
  57. 57. Essays UK. The History of Image Processing Information Technology Essay. 2013. Available from:
  58. 58. Yusuf AS, Sarfraz M. Color Edge Enhancement Based Fuzzy Segmentation of License Plates, the Proceedings of IEEE International Conference on Information Visualisation (IV’2005)-UK. Los Alamitos, USA: IEEE Computer Society Press; 2005. 991-996
  59. 59. Ahmed MJ, Sarfraz M, Zidouri A, Alkhatib WG. License Plate Recognition System, the Proceedings of the 10th IEEE International Conference on Electronics, Circuits and Systems (ICECS2003). Sharjah, United Arab Emirates (UAE): IEEE; 2003
  60. 60. Sarfraz M, Ahmed M, Ghazi SA. Saudi Arabian License Plate Recognition System, the Proceedings of IEEE International Conference on Geoemetric Modeling and Graphics-GMAG’2003-UK. USA: IEEE Computer Society Press; 2003

Written By

Muhammad Sarfraz

Published: 13 May 2020