Table of Contents23 Image Formation & Display:Digital Image StructureCameras and EyesTelevision Video SignalsOther Image Acquisition and DisplayBrightness and Contrast AdjustmentsGrayscale TransformsWarping373CHAPTER23Image Formation & DisplayImages are a description of how a parameter varies over a surface. For example, standard visualimages result from light intensity variations across a two-dimensional plane. However, light isnot the only parameter used in scientific imaging. For example, an image can be formed of thetemperature of an integrated circuit, blood velocity in a patient's artery, x-ray emission from adistant galaxy, ground motion during an earthquake, etc. These exotic images are usuallyconverted into conventional pictures (i.e., light images), so that they can be evaluated by thehuman eye. This first chapter on image processing describes how digital images are formed andpresented to human observers. Digital Image StructureFigure 23-1 illustrates the structure of a digital image. This example image isof the planet Venus, acquired by microwave radar from an orbiting spaceprobe. Microwave imaging is necessary because the dense atmosphere blocksvisible light, making standard photography impossible. The image shown isrepresented by 40,000 samples arranged in a two-dimensional array of 200columns by 200 rows. Just as with one-dimensional signals, these rows andcolumns can be numbered 0 through 199, or 1 through 200. In imaging jargon,each sample is called a pixel, a contraction of the phrase: picture element.Each pixel in this example is a single number between 0 and 255. When theimage was acquired, this number related to the amount of microwave energybeing reflected from the corresponding location on the planet's surface. Todisplay this as a visual image, the value of each pixel is converted into agrayscale, where 0 is black, 255 is white, and the intermediate values areshades of gray. Images have their information encoded in the spatial domain, the imageequivalent of the time domain. In other words, features in images arerepresented by edges, not sinusoids. This means that the spacing andnumber of pixels are determined by how small of features need to be seen,The Scientist and Engineer's Guide to Digital Signal Processing374rather than by the formal constraints of the sampling theorem. Aliasing canoccur in images, but it is generally thought of as a nuisance rather than a majorproblem. For instance, pinstriped suits look terrible on television because therepetitive pattern is greater than the Nyquist frequency. The aliasedfrequencies appear as light and dark bands that move across the clothing as theperson changes position. A "typical" digital image is composed of about 500 rows by 500 columns. Thisis the image quality encountered in television, personnel computer applications,and general scientific research. Images with fewer pixels, say 250 by 250, areregarded as having unusually poor resolution. This is frequently the case withnew imaging modalities; as the technology matures, more pixels are added.These low resolution images look noticeably unnatural, and the individualpixels can often be seen. On the other end, images with more than 1000 by1000 pixels are considered exceptionally good. This is the quality of the bestcomputer graphics, high-definition television, and 35 mm motion pictures.There are also applications needing even higher resolution, requiring severalthousand pixels per side: digitized x-ray images, space photographs, and glossyadvertisements in magazines.The strongest motivation for using lower resolution images is that there arefewer pixels to handle. This is not trivial; one of the most difficult problemsin image processing is managing massive amounts of data. For example, onesecond of digital audio requires about eight kilobytes. In comparison, onesecond of television requires about eight Megabytes. Transmitting a 500 by500 pixel image over a 33.6 kbps modem requires nearly a minute! Jumpingto an image size of 1000 by 1000 quadruples these problems. It is common for 256 gray levels (quantization levels) to be used in imageprocessing, corresponding to a single byte per pixel. There are several reasonsfor this. First, a single byte is convenient for data management, since this ishow computers usually store data. Second, the large number of pixels in animage compensate to a certain degree for a limited number of quantizationsteps. For example, imagine a group of adjacent pixels alternating in valuebetween digital numbers (DN) 145 and 146. The human eye perceives theregion as a brightness of 145.5. In other words, images are very dithered. Third, and most important, a brightness step size of 1/256 (0.39%) is smallerthan the eye can perceive. An image presented to a human observer will notbe improved by using more than 256 levels. However, some images need to be stored with more than 8 bits per pixel.Remember, most of the images encountered in DSP represent nonvisualparameters. The acquired image may be able to take advantage of morequantization levels to properly capture the subtle details of the signal. Thepoint of this is, don't expect to human eye to see all the information containedin these finely spaced levels. We will consider ways around this problemduring a later discussion of brightness and contrast.The value of each pixel in the digital image represents a small region in thecontinuous image being digitized. For example, imagine that the VenusChapter 23- Image Formation and Display 375FIGURE 23-1Digital image structure. This exampleimage is the planet Venus, as viewed inreflected microwaves. Digital imagesare represented by a two-dimensionalarray of numbers, each called a pixel. Inthis image, the array is 200 rows by 200columns, with each pixel a numberbetween 0 to 255. When this image wasacquired, the value of each pixelcorresponded to the level of reflectedmicrowave energy. A grayscale imageis formed by assigning each of the 0 to255 values to varying shades of gray.183 183 181 184 177 200 200 189 159 135 94 105 160 174 191 196186 195 190 195 191 205 216 206 174 153 112 80 134 157 174 196194 196 198 201 206 209 215 216 199 175 140 77 106 142 170 186184 212 200 204 201 202 214 214 214 205 173 102 84 120 134 159202 215 203 179 165 165 199 207 202 208 197 129 73 112 131 146203 208 166 159 160 168 166 157 174 211 204 158 69 79 127 143174 149 143 151 156 148 146 123 118 203 208 162 81 58 101