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Law Transformation in digital image processing with examples - YouTube YouTube Start of suggested clip End of suggested clip Series this is anushri and today i'm going to explain logarithmic transformation and power lawMoreSeries this is anushri and today i'm going to explain logarithmic transformation and power law transformation. So if you haven't watched the previous. Videos yet then kindly do so watch those videos

The inverse log transform expands the values of light-level pixels while compressing the darker-level values. Note: s,r: denote the gray level of the input pixel and the output pixel. 'c' is a constant; to map from [0,255] to [0,255], c =LOG(256)/255.

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.

Dynamic range refers to the ratio of max and min intensity values. When the dynamic range of the image is greater than that of displaying device(like in Fourier transform), the lower values are suppressed. To overcome this issue, we use log transform.

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 .

The log transformation is often used to reduce skewness of a measurement variable. If, after transformation, the distribution is symmetric, then the Welch t-test might be used to compare groups. If, also, the distribution becomes close to normal, then a reference interval might be determined.

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.