Digital Image Processing Lecture9: Intensity (Gray-level) Transformation Functions
Digital Image Processing Lecture9: Intensity (Gray-level) Transformation Functions using MATLAB Function imadjust Function imadjust is the basic IPT tool for intensity transformations of gray-scale images. It has the syntax: g = imadjust (f, [low_in high_in], [low_out high_out], gamma) Function imadjust
As illustrated in figure 3.2 (above), this function maps the intensity values in image f to new values in g, such that values between low_in and high_in map to values between low_out and high_out. Values below low_in and above high_in are clipped; that is values below low_in map to low_out, and those above high_in map to high_out.
Function imadjust The input image can be of class uint8, uint16, or double, and the output image has the same class as the input. All inputs to function imadjust, other than f, are specified as values between 0 and 1, regardless of the class of f. If f is of class uint8,
imadjust multiplies the value supplied by 255 to determine the actual values to use; if f is of class uint16, the values are multiplied by 65535. Using the empty matrix ([ ]) for [low_in high_in] of for [low_out high_out] results in the default values [0 1].
If high_out is less than low_out, the output intensity is reversed. Function imadjust Parameter gamma specifies the shape of the curve that maps the intensity values of f to create g. If gamma is less than 1, the mapping is weighted toward higher (brighter) output values, as fig 3.2 (a) shows. If gamma is greater than 1, the mapping is weighted toward lower (darker) output values. If it is omitted from the function arguments, gamma defaults to 1 (linear mapping).
Function imadjust Examples Example1: >> f = imread ('baby-BW.jpg'); >> g = imadjust (f, [0 1], [1 0]); >> imshow(f), figure, imshow (g); >> imshow(f), figure, imshow (g);
f g Function imadjust Examples Cont. Example1: This process, which is the digital equivalent of obtaining a
photographic negative, is particularly useful for enhancing white or gray detail embedded in a large dark region. The negative of an image can be obtained also with IPT function imcomplement: g = imcomplement (f); Function imadjust Examples Example2:
>> g = imadjust (f, [0.5 0.75], [0 1], .5); >> imshow(f), figure, imshow (g); f g Function imadjust Examples
Example3: >> g = imadjust (f, [0.5 0.75], [0.6 1], 0.5); >> imshow(f), figure, imshow (g); f g Function imadjust Examples
Example4: >> g = imadjust (f, [ ], [ ], 2); >> imshow(f), figure, imshow (g); f g Logarithmic Transformations
The shape of logarithmic function curve, is similar to the gamma curve where gamma < 1, where low values set to 0 and high values set to 1 on both scales. The shape of the gamma curve is variable (because of the change of low and high values), while the shape of the log function is fixed.
Logarithmic transformations are implemented using the expression: g = c * log (1 + double (f)) Logarithmic Transformations But this function changes the data class of the image to double, so another sentence to return it back to uint8 should be done: gs = im2uint8 (mat2gray(g));
Use of mat2gray brings the values to the range [0 1] and im2uint8 brings them to the range [0 255] Logarithmic Transformations Example: >> g = log(1 + double(f)); >> gs = im2uint8(mat2gray(g));
>> imshow(f), figure, imshow (g), figure, imshow(gs); f g gs Contrast-Stretching Transformation The function shown in the figure below, as indicated in lecture 8, is called a
contrast-stretching transformation function, because it compresses the input levels lower than m into a narrow range of dark levels in the output image. Similarly, it compresses the values above m into a narrow band of light levels in the output. The result is an image of higher contrast. The limiting case shown below (b), shows the output of a binary image. This function is called Thresholding, as mentioned earlier. Thresholding, is a simple tool that can be used for image segmentation. Contrast-Stretching Transformation The function takes the form of:
Where r represents the intensities of the input image, s the corresponding intensity values in the output image, and E controls the slope of the function. Contrast-Stretching Transformation This equation is implemented in MATLAB for the entire image as Note the use of eps to prevent overflow if f has any 0
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