Page 112 -
P. 112

3.1 Point operators                                                                     91


                                                                                        160
                                                                  45  60  98  127 132 133 137 133  140
                                                                                         120
                                                                  46  65  98  123 126 128 131 133
                                                                                         100
                                                                  47  65  96  115 119 123 135 137  range  80
                                                                  47  63  91  107 113 122 138 134  60
                                                                  50  59  80  97  110 123 133 134  40
                                                                                         20
                                                                  49  53  68  83  97  113 128 133  0    15
                                                                  50  50  58  70  84  102 116 126  S16  S15  S14  S13  S12  S11  9  11  domain 13
                                                                                           domain  S10  S9  S8  S7  S6  5  7
                                                                  50  50  52  58  69  86  101 120  S5  S4  S3  S2  S1  1  3
                           (a)                     (b)                    (c)                  (d)
               Figure 3.3 Visualizing image data: (a) original image; (b) cropped portion and scanline plot using an image in-
               spection tool; (c) grid of numbers; (d) surface plot. For figures (c)–(d), the image was first converted to grayscale.


               3.1.1 Pixel transforms

               A general image processing operator is a function that takes one or more input images and
               produces an output image. In the continuous domain, this can be denoted as

                                g(x)= h(f(x)) or g(x)= h(f 0 (x),...,f n (x)),       (3.1)
               where x is in the D-dimensional domain of the functions (usually D =2 for images) and the
               functions f and g operate over some range, which can either be scalar or vector-valued, e.g.,
               for color images or 2D motion. For discrete (sampled) images, the domain consists of a finite
               number of pixel locations, x =(i, j), and we can write

                                            g(i, j)= h(f(i, j)).                     (3.2)

               Figure 3.3 shows how an image can be represented either by its color (appearance), as a grid
               of numbers, or as a two-dimensional function (surface plot).
                  Two commonly used point processes are multiplication and addition with a constant,
                                             g(x)= af(x)+ b.                         (3.3)

               The parameters a> 0 and b are often called the gain and bias parameters; sometimes these
                                                                                    1
               parameters are said to control contrast and brightness, respectively (Figures 3.2b–c). The
               bias and gain parameters can also be spatially varying,
                                          g(x)= a(x)f(x)+ b(x),                      (3.4)

               e.g., when simulating the graded density filter used by photographers to selectively darken
               the sky or when modeling vignetting in an optical system.
                  Multiplicative gain (both global and spatially varying) is a linear operation, since it obeys
               the superposition principle,
                                        h(f 0 + f 1 )= h(f 0 )+ h(f 1 ).             (3.5)

               (We will have more to say about linear shift invariant operators in Section 3.2.) Operators
               such as image squaring (which is often used to get a local estimate of the energy in a band-
               pass filtered signal, see Section 3.5) are not linear.

                  1  An image’s luminance characteristics can also be summarized by its key (average luminanance) and range
               (Kopf, Uyttendaele, Deussen et al. 2007).
   107   108   109   110   111   112   113   114   115   116   117