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Log transformation can help to ?stretch out? the tail of a right-skewed distribution, making it more symmetric and easier to analyze. In Python, the log transformation can be applied using the numpy library.

LOG transform enhances small magnitude input values into wider range of output pixel values and compresses large magnitude input values into narrow range of output values.

The log transformation is, arguably, the most popular among the different types of transformations used to transform skewed data to approximately conform to normality. If the original data follows a log-normal distribution or approximately so, then the log-transformed data follows a normal or near normal distribution.

Log transformation is used for image enhancement as it expands dark pixels of the image as compared to higher pixel values. The formula for applying log transformation in an image is, S = c * log (1 + r) where, R = input pixel value, C = scaling constant and S = output pixel value.

In Python, the log transformation can be applied using the numpy library. The numpy library provides a natural logarithm function, np. log() , which can be used to apply the log transformation to an array of data.

As in log transformation, power- law curves with. Figure(5.16) For ?> maps a narrow range of dark i/p values into a wider range of o/p values and with the opposite being true for higher values of i/p. By varying we obtain a family of possible transformation: For curves generated with values ?>effect is opposite to .

Log transformation s = c log(r + 1). Where s and r are the pixel values of the output and the input image and c is a constant. The value 1 is added to each of the pixel value of the input image because if there is a pixel intensity of 0 in the image, then log (0) is equal to infinity.