Chapter 3 Image enhancement in the spatial domain,It makes all the difference whether one sees darkness through the light or brightness through the shadows. David Lindsay,3.1 Background,The objective of enhancement: More suitable for a specific application. interesting and visually appealing areas of DIP Two broad categories of image enhancement techniques Spatial domain techniques (chapter 3) The aggregate of pixels composing an image Direct manipulation of image pixels Frequency domain techniques (chapter 4) Manipulation of Fourier transform or wavelet transform of an image No general theory of image enhancement, subjective,3.1 Background,Spatial domain processing: Based on direct manipulation of pixels in an image. where f (x, y) is the input image, g(x, y) is the processed image, and T is an operator on f, defined over some neighborhood of (x, y) . In addition, T can operate on a set of input images, such as performing the pixel-by-pixel sum of K images for noise reduction, as discussed in Section 3.4.2.,The principal approach in defining a neighborhood about a point (x, y) is to use a square or rectangular subimage area centered at (x, y).,neighborhood about a point:,Point operation (processing): - The simplest form of T is when the neighborhood is of size 1*1 (that is, a single pixel). In this case, g depends only on the value of f at (x, y) , and T becomes a gray-level (also called an intensity or mapping) transformation function of the form: - where, for simplicity in notation, r and s are variables denoting, respectively, the gray level of f (x, y) and g(x, y) at any point (x, y).,Contrast stretching: the effect to produce an image of higher contrast than the original by darkening the levels below m and brightening the levels above m in the original image.(Fig. 3.2(a) Thresholding limiting case shown in Fig. 3.2(b), T(r) produces a two-level (binary) image. A mapping of this form is called function,point processing: enhancement at any point in an image depends only on the gray level at that point. Filtering (mask processing): Section 3.5 The general approach is to use a function of the values of f in a predefined neighborhood of (x, y) to determine the value of g at (x, y) masks (filters, kernels, templates, or windows) A mask is a small (say, 3*3) 2-D array, in which the values of the mask coefficients determine the nature of the process, such as image sharpening.,3.2 Some Basic Gray Level Transformations,Values stroage: 1D array mappings:table lookups. Eg: an 8-bit environment, a lookup table containing the values of T will have 256 entries,3.2.1 Image negatives The negative of an image with gray levels in the range 0,L-1 is obtained by using the negative transformation shown in Fig. 3.3, which is given by the expression s = L - 1 r (3.2-1),particularly suited for enhancing white or gray detail embedded in dark regions of an image, especially when the black areas are dominant in size. much easier it is to analyze the breast tissue in the negative image,3.2.2 Log transformations,The general form of the log transformation shown in Fig. 3.3 is where c is a constant, and it is assumed that r = 0. The shape shows that this transformation maps a narrow range of low gray-level values in the input image into a wider range of output levels. The opposite is true of higher values of input levels. We would use a transformation of this type to expand the values of dark pixels in an image while compressing the higher-level values. The opposite is true of the inverse log transformation.,Fig. 3.5(a) : a Fourier spectrum with values in the range 0 to 1.5*106 Fig. 3.5(b) : the range becomes 0 to 6.2, a more manageable number.,A classic application: Fourier spectrum, range from 0 to 106 or higher no problems for a computer while processing numbers will not be able to reproduce faithfully image display systems The net effect: a significant degree of detail will be lost,3.2.3 Power-Law transformation,Power-law transformations have the basic form where c and are positive constants. Sometimes written as s = c(r + ) to account for an offset (that is, a measurable output when the input is zero). However, offsets typically are an issue of display calibration and as a result they are normally ignored,power-law curves with fractional values of map a narrow range of dark input values into a wider range of output values, with the opposite being true for higher values of input levels 1: spreading 1: compressing Log: compresses the dynamic range with large variations Power-law: a family of possible transformation curves by varying ,Gamma correction A variety of devices used for image capture, printing, and display respond according to a power law. The process used to correct this power-law response phenomena is called gamma correction. For example, cathode ray tube (CRT) devices have an intensity-to-voltage response that is a power function, with exponents varying from approximately