Improved version of traditional median filtering Having two parts: Noise detection and median filtering\n To determine the noise pixels, we defined a simple criterias It is being called adaptive because it changes it property after each iterations
Histogram equalization is a basic image processing technique that adjusts the global contrast of an image by updating the image histogram’s pixel intensity distribution. Doing so enables areas of low contrast to obtain higher contrast in the output image. Essentially, histogram equalization works by: • Computing a histogram of image pixel intensities • Evenly spreading out and distributing the most frequent pixel values (i.e., the ones with the largest counts in the histogram) • Giving a linear trend to the cumulative distribution function (CDF)
• CLAHE limits the amplification by clipping the histogram at a predefined value before computing
the CDF.
• In this we limit the slope of transformation function by a so called clip limit.
• It is advantageous not to discard the part of the histogram that exceeds the clip limit but to
redistribute it equally among all histogram bins
