Modify Image Log For Free

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 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.

C = 255/(log(1 + max_input_pixel_value)) The value of c is chosen such that we get the maximum output value corresponding to the bit size used. e.g for 8 bit image, c is chosen such that we get max value equal to 255. Clearly, the low intensity values in the input image are mapped to a wider range of output levels.

Gray level transformation allows the modification of pixel intensities by mapping input gray levels to various output levels, to achieve desired image enhancements.

Basically, applying a log10 transformation, for instance, makes the sequence 1, 10, 100, 1000 and 10000 be transformed to 0, 1, 2, 3, 4 and 5. If you are not familiar with logarithms, take a look at Introduction to Logarithms (by Khan Academy).

Log transformation of an image means replacing all pixel values, present in the image, with its logarithmic values. Log transformation is used for image enhancement as it expands dark pixels of the image as compared to higher pixel values.

The log transformation tends to feature prominently for working with right-skewed data. Since log(0) returns -Infinity , a common first reaction is to use log(y + c) as the response in place of log(y) , where c is some constant added to the y variable to get rid of the 0 values.