Compare the resultĪs with other image arithmetic operations, it is Operations, the text could become quite legible.
Not fantastic, with a little work using morphological Thresholding this image at a pixel value of 160. Virtually all the illumination gradient has been removed. Note that floating pointįormat images were used in the division, which were thenĭisplay. Therefore the division should allow us to segment the letters out Reflectance of the blank paper to be uniform over its surface, then: Using subscripts to distinguish the blank (lightfield) image and theīut since I(x,y) is the same for both images, and assuming the Reflectance R(x,y) of the scene at that point and also on the Y, then the reflected light intensity B(x,y) depends upon the On the surface of the scene described by coordinates x and Now, assuming that we are dealing with a flat scene here, with points To capture the incident illumination variation. Take a picture of a blank sheet of white paper which should allow us Situation arises quite a lot in microscopy, for instance. Take several images with different items in the viewfield. Suppose that we cannot change the lighting conditions, but that we can There is no global threshold value that works over the Shows the result of straightforward intensity thresholding at a pixel Illumination gradient across the image which makes conventionalįoreground/background segmentation using standard Shows a poorly illuminated piece of text. Pixel division is to separate the actual reflectance of an object from Produces approximately the same pixel values at the old and the new position of a moved part. Due to noise, the image also shows the position of objectsįor comparison, the absolute difference between the two images, as shown in Intermediate graylevels in the equalized image correspond to areas of Intensity of the moved object is lower than the background intensity. Position, low values correspond to the old position, assuming that the Histogram equalizing the division output, as shown in The reason why we can only see the new position of the moved part in The pixel value in the second image is not smaller than 1). Value increased after the first exposure the result of the division isĬlustered between 0 and 1, otherwise it is between 1 and 255 (provided Dividing theįormer by the latter using a floating point pixel type and thenĬontrast stretching the resulting image yieldsĬhange between the exposures have a value of 1, whereas if the pixel Objects have been slightly moved between the exposures. Or ratio between corresponding pixel values (hence the commonĪlternative name of ratioing). One frame to the next, however, division gives the fractional change Instead of giving the absolute change for each pixel from In a similar way to the use of subtraction for the One of the most important uses of division is in
Value types other than simply 8-bit integers comes in very handy when If only integerĭivision is performed, then results are typically rounded down to the The division operator may only implement integer division, or it mayĪlso be able to handle floating point division. red, blueĪnd green components) are simply divided separately to produce the for color images) than the individual components ( e.g. If the pixel values are actually vectors rather than scalar values The division of two images is performed in the obvious way in a singleĭivision by a constant is performed using: Image, in which case every pixel value in that image is divided by a Many implementations can also be used with just a single input Produces a third whose pixel values are just the pixel values of theįirst image divided by the corresponding pixel values of the second The image division operator normally takes two images as input and
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