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The natural logarithm (log) is calculated using the numpy. log() function in Python. The logarithm with a base other than e can be calculated using the numpy. log10() or numpy.

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. When we apply log transformation in an image and any pixel value is '0' then its log value will become infinite.

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.

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.

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.

Understanding the log() functions in Python We need to use the math module to access the log functions in the code. The math. log(x) function is used to calculate the natural logarithmic value i.e. log to the base e (Euler's number) which is about 2.71828, of the parameter value (numeric expression), passed to it.