What is digital image processing? Give examples

An image may be defined as a two-dimensional function,where x andy are spatial coordinates, and the amplitude of at any pair of coordinatesis called the intensity or gray level of the image at that point. When x, y,and the amplitude values of are all finite, discrete quantities, we call theimage a digital image. The field of digital image processing refers to processingdigital images by means of a digital computer. Note that a digital image is com-posed of a finite number of elements, each of which has a particular locationand value. These elements are referred to as picture elements, image elements,pels, and pixels. Pixel is the term most widely used to denote the elements of adigital image.

Vision is the most advanced of our senses, so it is not surprising that imagesplay the single most important role in human perception. However, unlike hu-mans, who are limited to the visual band of the electromagnetic (EM) spec-trum, imaging machines cover almost the entire EM spectrum, ranging fromgamma to radio waves. They can operate also on images generated by sourcesthat humans are not accustomed to associating with images. These include ul-trasound, electron microscopy, and computer-generated images. Thus, digitalimage processing encompasses a wide and varied field of applications.There is no general agreement among authors regarding where image pro-cessing stops and other related areas, such as image analysis and computer vi-sion, start. Sometimes a distinction is made by defining image processing as adiscipline in which both the input and output of a process are images.

We be-lieve this to be a limiting and somewhat artificial boundary. For example,under this definition, even the trivial task of computing the average intensityof an image would not be considered an image processing operation. On theother hand, there are fields such as computer vision whose ultimate goal is touse computers to emulate human vision, including learning and being able tomake inferences and take actions based on visual inputs. This area itself is abranch of artificial intelligence (AI), whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in between image processing and computer vision. There are no clear-cut boundaries in the continuum from image processingat one end to computer vision at the other. However, one useful paradigm is toconsider three types of computerized processes in this continuum: low-, mid-,and high-level processes. Low-level processes involve primitive operationssuch as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputsand outputs are images. Mid-level processes on images involve tasks such as segmentation (partitioning an image into regions or objects), description ofthose objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A mid-level process is charac-terized by the fact that its inputs generally are images, but its outputs are at-tributes extracted from those images (e.g., edges, contours, and the identity ofindividual objects). Finally, higher-level processing involves “making sense” ofan ensemble of recognized objects, as in image analysis, and, at the far endof the continuum, performing the cognitive functions normally associated withhuman vision.Based on the preceding comments, we see that a logical place of overlap be-tween image processing and image analysis is the area of recognition ofindividual regions or objects in an image. Thus, what we call in this book digitalimage processing encompasses processes whose inputs and outputs are imagesand,in addition, encompasses processes that extract attributes from images, upto and including the recognition of individual objects.