数字图像处理第三章(精品·公开课件).ppt

Digital Image Processing 3 Image Enhancement in the Spatial Domain Preview The aim of enhancement is to improve images For visual interpretation For machine perception Image enhancement methods include two sides: Spatial domain methods Frequency domain methods Visual evaluation of image quality Object Subject 3.1 Background Definition of spatial domain processes Spatial domain processes will be denoted by the expression g(x, y) = T[ f (x, y)] 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). 3.1 Background Neighborhood 3.1 Background The point approach The neighborhood is of size 1 ? 1. Gray-level transformation function s = T (r) 　where, r is the gray-level of f (x, y) 　and s is the gray-level of g(x, y). The gray-level transformation function is also called intensity or mapping transformation function. 3.1 Background Gray-level transformation function 3.1 Background The template approach A function of the values of f in a predefined neighborhood of (x, y) is used to determine the value of g at (x, y). The template is also called mask, filter, kernel or window. The mask processing includes averaging and sharpening. 3.2 Some Gray-level transformations Some basic transformations Identity Negative Log Inverse log Root Power 3.2.1 image negatives Negative transformations 3.2.2 Log transformations Log transformations 3.2.3 Power-law transformations Power-law transformations s = cr? 3.2.3 Power-law transformations Gamma correction of monitor display 3.2.3 Power-law transformations Example 3.2.3 Power-law transformations Another example 3.2.4 Piecewise-linear transformation Contrast stretching 3.2.4 Piecewise-linear transformation Example of contrast stretching 3.2.4 Piecewise-linear transformation Gray-level slicing 3.2.4 Piecewise-linear transformation Bit-plane slicing 3.2.4 Piecewise-linear transformation Bit-plane slicing 3.2.4 Pi