In this exercise you use three image processing functions to improve the image clarity of a grayscale aerial photograph.
Several editing filters are available in AutoCAD Raster Design toolset. Those that use the histogram include functions to convert color images to grayscale or bitonal (black and white) images, and to convert grayscale images to bitonal images.
In this lesson, you experiment with the Brightness, Contrast, and Equalize functions. You can observe the effects of each function on the image by using both the preview and histogram display before you apply the changes.
In this exercise you process the whole image. AutoCAD Raster Design toolset also allows only a specified portion of the image to be processed.
Before doing this exercise, ensure that AutoCAD Raster Design toolset options are set as described in the exercise Exercise A1: Setting AutoCAD Raster Design Toolset Options.
Adjust the contrast and the brightness
Ensure that Brightness/Contrast is the active tab.
The frequency value identifies the number of pixels in the image assigned to this shade of gray. In the next few steps, you will see the effects of the contrast and brightness controls.
Increasing the contrast displays mid-tone image values in more extreme light and dark shades. Increasing the contrast to an extreme value in a grayscale image polarizes the shades of gray to display in black and white.
Decreasing the contrast in a grayscale image displays more image values in midrange shades. At extreme low contrast, all the image values display as a single shade of gray.
In grayscale images, increasing the brightness displays gray image values in lighter shades. At extreme brightness, all image values display as white.
In grayscale images, decreasing the brightness displays image values in darker shades. In the extreme case, all image values display as black.
Equalize the grayscale of the image
Equalization is useful for images in which a large percentage of pixels are approximately the same color. In a grayscale image, equalization changes the darkest pixels to black and the lightest pixels to white and then reassigns the remaining pixels to use all shades in between.
Note that image details are more visible in the equalized image.