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Digital Image Processing

October 16, 2017
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Digital image processing is concerned primarily with extracting useful information from images. Ideally, this is done by computers, with little or no human intervention. Image processing algorithms may be placed at three levels. At the lowest level are those techniques which deal directly with the raw, possibly noisy pixel values, with de-noising and edge detection being good examples.

Digital image processing : Definition

Digital image processing is the manipulation of the numeric data of the digital image for enhancing it to make it suitable for the further processing according to the specific application needs. Today, Image Processing systems are very popular due to easy availability of powerful computers, large size memory devices, graphics software etc. By doing image processing, corrupted pictures can be enhanced, medical images clarified, and satellite photographs improved. Image Processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space and aircrafts or pictures taken in normal day-to-day life for various applications. A digital image is an array of real numbers represented by a finite number of bits. Basic operation of image processing can be shown with the help of figure 1.

Digital Image Processing

Figure 1: Basic Operations

While acquiring the image from the source like sensor, digital camera etc. there may occur some disturbances. These are called as noise. Disturbances may occur due to bad weather conditions or some other interference while capturing the image. The net effect is a corrupted image that needs to be pre-processed to reduce or eliminate the noise. Although noise gives an image a generally undesirable appearance, the most significant factor is that noise can cover and reduce the visibility of certain features within the image. The loss of visibility is especially significant for low-contrast objects. In addition, sometimes images are not of good quality, due to both hardware and software inadequacies. So, they have to be enhanced and improved before other analysis can be performed on them. Before processing the image we have to remove the unwanted data from it, it means we have to remove the noise from the image. There are different types of noises each having different statistical properties.

Color image processing is an area that has been gaining in importance because of the important increase in the use of digital images over the internet as well as use of image processing Toolbox and extends some of its functionality by developing additional colour generation and transformation function. Wavelet and multi-thresholding processing can be used in task ranging from edge detection to image smoothing. The fundamental of image compression is to compress the image by removal of coding, inter pixel, psyconvisual redundancy. In image processing, morphology is all about regions and shapes. In image processing, morphology is all about regions and shapes. In fact, it is used as a tool for extracting image components that are useful in signifying regions and shape. Dilation and Erosion are fundamental operations of morphological image processing.

Application of Digital Image Processing

Digital Image Processing is applied in the fields of Computer vision, Face detection, Feature detection, Lane departure warning system, Non-photorealistic rendering, Medical image processing, Microscope image processing Morphological image processing, Remote sensing, etc. Visual information is the most important type of information perceived, processed and interpreted by the human brain. One third of the cortical area of the human brain is dedicated to visual information processing. Digital image processing, as a computer-based technology, carries out automatic processing, manipulation and interpretation of such information, and it plays an increasingly important role in many aspects of our daily life, as well as in a wide variety of disciplines and fields in science and technology, with applications such as television, photography, robotics, remote sensing, medical diagnosis and industrial inspection.

  • Detecting events (e.g., for visual surveillance or people counting).
  • Organizing information (e.g., for indexing databases of images and image sequences).
  • Modelling objects or environments (e.g., industrial inspection, medical image analysis or topographical modelling).
  • Computerized photography (e.g., Photoshop)
  • Space image processing (e.g., Hubble space telescope images, interplanetary probe images)
  • Medical/Biological image processing (e.g., interpretation of X-ray images, blood/cellular microscope images)
  • Automatic character recognition (zip code, license plate recognition)
  • Finger print/face/iris recognition
  • Remote sensing: aerial and satellite image interpretations
  • Reconnaissance
  • Industrial applications (e.g., product inspection/sorting


[1] Castleman Kenneth R, Digital Image Processing, Prentice Hall, New Jersey.

[2] Rafael Gonzalez, Richard Woods, “Digital Image Processing”, Pearson Publications, 2002.

[3] Vinod Sharma, Deepika Bansal, “A review on digital image enhancement by noise removal”, IJIRSET, vol.4, may 2015.

[4] A. K. Jain “Fundamentals of digital image processing”. Prentice-Hall, 1989


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