Image enhancement Image enhancement is the use of image processing algorithms to ertain types of distortion in an mage. The image is enhanced by removing noise, making the edge s in the image stand out, or an other operation that makes the image look better. Point operation bove are generally considered to be enhancement operations Enhancement also includes operations that use groups of pixels and the spatial location of the pixels in the image. The most widely used algorithms for enhancement are based on pixel functions that are known as window operations. a window operation performed on an image is nothing more than the process of examining the pixels in a certain region of the image, called the window region, and computing some type of mathematical function derived from the pixels in the window. In most cases the windows are square or rectangle, although other shapes have been used. After the operation is performed, the result of the computation is placed in the center pixel of the window where a 3 x 3 pixel window has been extracted from the image. The values of the pixels in the window, labeled a1, a2,..., ag, are used to compute a new pixel value which replaces the value of as, and the window is moved to a new center location until all the pixels in the original image have been processed. As an example of a window operation, suppose we computed the average value of the pixels in the ndow. This operation is known as smoothing and will tend to reduce noise in the image, but unfortunately it will also tend to blur edge structures in the image. Another window operation often used is the computation of a linear weighted sum of the pixel values. Let d s be the new pixel value that will replace a, in the original image. We then form C: a (17.1) where the a s are any real numbers. For the simple smoothing operation described above we set a =1/9 for all i. By changing the values of the a; weights, one can perform different types of enhancement operations to an image. Any window operation that can be described by Eq 17. 1 is known as a linear window operation or convolution operator. If some of the a; coefficients take on negative values, one can enhance the appearance of edge structures in the image. It is possible to compute a nonlinear function of the pixels in the window. One of the more powerful nonline window operations is that of median filtering. In this operation all the pixels in the window are listed middle, or median, pixel is obtained. The median pixel then is used to replace as. The median filter is used to remove noise from an image and at the same time preserve the edge structure the image. More recently there has been a great deal of interest in morphological operators. These are also nonlinear window operations that can be used to extract or enhance shape information in an image. In the preceding discussion, all of the window operations were described on 3 x 3 windows. The current research in window operations is directed at using large window sizes, i.e., 9 x 9, 13 X 13, or 21 x 21. The philosophy in this work is that small window sizes only use local information and what one really needs to use is information that is more global in nature. Digital Image Compression Image compression refers to the task of reducing the amount of data required to store or transmit a digital Issed earlier, in its natural form, a digital image comprises an array of numbers. Each such cement is often confused with image restoration Image enhancement is the ac processing algorithms to enhance the appearance of the image. Image restoration is the application of algorithn knowledge of the degradation process to enhance or restore the image, i.e., deconvolution algorithms used to effect of the aperture point spread function in blurred images. a discussion of image restoration is beyond the scope of this c2000 by CRC Press LLC© 2000 by CRC Press LLC Image Enhancement Image enhancement is the use of image processing algorithms to remove certain types of distortion in an image. The image is enhanced by removing noise, making the edge structures in the image stand out, or any other operation that makes the image look better. 1 Point operations discussed above are generally considered to be enhancement operations. Enhancement also includes operations that use groups of pixels and the spatial location of the pixels in the image. The most widely used algorithms for enhancement are based on pixel functions that are known as window operations. A window operation performed on an image is nothing more than the process of examining the pixels in a certain region of the image, called the window region, and computing some type of mathematical function derived from the pixels in the window. In most cases the windows are square or rectangle, although other shapes have been used. After the operation is performed, the result of the computation is placed in the center pixel of the window where a 3 3 3 pixel window has been extracted from the image. The values of the pixels in the window, labeled a1, a2, . . ., a9, are used to compute a new pixel value which replaces the value of a5, and the window is moved to a new center location until all the pixels in the original image have been processed. As an example of a window operation, suppose we computed the average value of the pixels in the window. This operation is known as smoothing and will tend to reduce noise in the image, but unfortunately it will also tend to blur edge structures in the image. Another window operation often used is the computation of a linear weighted sum of the pixel values. Let a9 5 be the new pixel value that will replace a5 in the original image. We then form (17.1) where the ai ’s are any real numbers. For the simple smoothing operation described above we set ai= 1/9 for all i. By changing the values of the ai weights, one can perform different types of enhancement operations to an image. Any window operation that can be described by Eq. 17.1 is known as a linear window operation or convolution operator. If some of the ai coefficients take on negative values, one can enhance the appearance of edge structures in the image. It is possible to compute a nonlinear function of the pixels in the window. One of the more powerful nonlinear window operations is that of median filtering. In this operation all the pixels in the window are listed in descending magnitude and the middle, or median, pixel is obtained. The median pixel then is used to replace a5. The median filter is used to remove noise from an image and at the same time preserve the edge structure in the image. More recently there has been a great deal of interest in morphological operators. These are also nonlinear window operations that can be used to extract or enhance shape information in an image. In the preceding discussion, all of the window operations were described on 3 3 3 windows. The current research in window operations is directed at using large window sizes, i.e., 9 3 9, 13 3 13, or 21 3 21. The philosophy in this work is that small window sizes only use local information and what one really needs to use is information that is more global in nature. Digital Image Compression Image compression refers to the task of reducing the amount of data required to store or transmit a digital image. As discussed earlier, in its natural form, a digital image comprises an array of numbers. Each such 1 Image enhancement is often confused with image restoration. Image enhancement is the ad hoc application of various processing algorithms to enhance the appearance of the image. Image restoration is the application of algorithms that use knowledge of the degradation process to enhance or restore the image, i.e., deconvolution algorithms used to remove the effect of the aperture point spread function in blurred images. A discussion of image restoration is beyond the scope of this section. ¢ = = a a  i i i 5 1 9 a