Image processing

(computer science) A technique in which the data from an image are digitized and various mathematical operations are applied to the data, generally with a digital computer, in order to create an enhanced image that is more useful or pleasing to a human observer, or to perform some of the interpretation and recognition tasks usually performed by humans. Also known as picture processing.

Manipulating data in the form of an image through several possible techniques. An image is usually interpreted as a two-dimensional array of brightness values, and is most familiarly represented by such patterns as those of a photographic print, slide, television screen, or movie screen. An image can be processed optically, or digitally with a computer.

To digitally process an image, it is first necessary to reduce the image to a series of numbers that can be manipulated by the computer. Each number representing the brightness value of the image at a particular location is called a picture element, or pixel. A typical digitized image may have 512 × 512 or roughly 250,000 pixels, although much larger images are becoming common. Once the image has been digitized, there are three basic operations that can be performed on it in the computer. For a point operation, a pixel value in the output image depends on a single pixel value in the input image. For local operations, several neighboring pixels in the input image determine the value of an output image pixel. In a global operation, all of the input image pixels contribute to an output image pixel value. These operations, taken singly or in combination, are the means by which the image is enhanced, restored, or compressed.

An image is enhanced when it is modified so that the information it contains is more clearly evident, but enhancement can also include making the image more visually appealing. An example is noise smoothing. To smooth a noisy image, median filtering can be applied with a 3 × 3 pixel window. This means that the value of every pixel in the noisy image is recorded, along with the values of its nearest eight neighbors. These nine numbers are then ordered according to size, and the median is selected as the value for the pixel in the new image. As the 3 × 3 window is moved one pixel at a time across the noisy image, the filtered image is formed.

Another example of enhancement is contrast manipulation, where each pixel's value in the new image depends solely on that pixel's value in the old image; in other words, this is a point operation. Contrast manipulation is commonly performed by adjusting the brightness and contrast controls on a television set, or by controlling the exposure and development time in printmaking. Another point operation is that of pseudocoloring a black-and-white image, by assigning arbitrary colors to the gray levels. This technique is popular in thermography (the imaging of heat), where hotter objects (with high pixel values) are assigned one color (for example, red), and cool objects (with low pixel values) are assigned another color (for example, blue), with other colors assigned to intermediate values.

The aim of restoration is also to improve the image, but unlike enhancement, knowledge of how the image was formed is used in an attempt to retrieve the ideal (uncorrupted) image. Any image-forming system is not perfect, and will introduce artifacts (for example, blurring, aberrations) into the final image that would not be present in an ideal image. A point spread function, called a filter, can be constructed that undoes the blurring. By imaging the blurred image with the filter point spread function, the restored image results. The filter point spread function is spread out more than the blurring point spread function, bringing more pixels into the averaging process. This is an example of a global operation, since perhaps all of the pixels of the blurred image can contribute to the value of a single pixel in the restored image. This type of deblurring is called inverse filtering, and is sensitive to the presence of noise in the blurred image. By modifying the deblurring filter according to the properties of the noise, performance can be improved. An example of the need to deblur images from an optical system is the Hubble Space Telescope before its spherical aberration was corrected with new optics. See also Satellite astronomy.

Compression is a way of representing an image by fewer numbers, at the same time minimizing the degradation of the information contained in the image. Compression is important because of the large quantities of digital imagery that are sent electronically and stored. Digital high-definition television relies heavily on image compression to enable transmission and display of large-format color images. Once the image is compressed for storage or transmission, it must be uncompressed for use, by the inverse of the compression operations. There is a trade-off between the amount of compression and the quality of the uncompressed image. High compression rates are acceptable with television images, for example. However, where high image quality must be preserved (as in diagnostic medical images), only compression rates as low as three to four may be acceptable. See also Data compression.

Image processing is an active area of research in such diverse fields as medicine, astronomy, microscopy, seismology, defense, industrial quality control, and the publication and entertainment industries. The concept of an image has expanded to include three-dimensional data sets (volume images), and even four-dimensional volume-time data sets. An example of the latter is a volume image of a beating heart, obtainable with x-ray computed tomography (CT). CT, PET, single-photon emission computed tomography (SPECT), MRI, ultrasound, SAR, confocal microscopy, scanning tunneling microscopy, atomic force microscopy, and other modalities have been developed to provide digitized images directly. Digital images are widely available from the Internet, CD-ROMs, and inexpensive charge-coupled-device (CCD) cameras, scanners, and frame grabbers. Software for manipulating images is also widely available. See also Charge-coupled devices; Compact disk; Computer storage technology; Computerized tomography; Confocal microscopy; Data communications; Digital computer; Electron microscope; Medical ultrasonic tomography; Nuclear medicine; Scanning tunneling microscope.

(1) The analysis of a picture using techniques that can identify shades, colors and relationships that cannot be perceived by the human eye. Image processing is used to solve identification problems, such as in forensic medicine or in creating weather maps from satellite pictures. It deals with images in bitmapped graphics format that have been scanned in or captured with digital cameras.

(2) Any image improvement, such as refining a picture in a paint program that has been scanned or entered from a video source.

(3) Same as imaging.

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Monochrome black/white image

In electrical engineering and computer science, image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.

Image processing usually refers to digital image processing, but optical and analog image processing are also possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging.

Contents 1 Typical operations 2 Applications 3 See also 4 External links

Typical operations

The red, green, and blue color channels of a photograph by Sergei Mikhailovich Prokudin-Gorskii. The fourth image is a composite.

Among many other image processing operations are:

• Euclidean geometry transformations such as enlargement, reduction, and rotation
• Color corrections such as brightness and contrast adjustments, quantization, or color translation to a different color space
• Digital compositing or optical compositing (combination of two or more images). Used in film-making to make a "matte"
• Interpolation, demosaicing, and recovery of a full image from a raw image format using a Bayer filter pattern
• Image registration, the alignment of two or more images
• Image differencing and morphing
• Image recognition, for example, extract the text from the image by using optical character recognition
• Image segmentation
• High dynamic range imaging by combining multiple images
• Geometric hashing for 2-D object recognition with affine invariance

Applications

Further information: Imaging

• Computer vision
• Face detection
• Feature detection
• Lane departure warning system
• Non-photorealistic rendering
• Medical image processing
• Microscope image processing
• Morphological image processing
• Remote sensing

See also

• Imaging
• Photo manipulation
• List of image analysis software
• Near sets

External links

• Lectures on Image Processing, by Alan Peters. Vanderbilt University. Updated 28 April 2008.
• Image Processing - Principles and Applications, by Tinku Acharya and Ajoy K. Ray, Wiley InterScience

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