A new algorithm to reduce noise in microscopy images implemented with a simple program in python

All microscopical images contain noise, increasing when (e.g., transmission electron microscope or light microscope) approaching the resolution limit. Many methods are available to reduce noise. One of the most commonly used is image averaging. We propose here to use the mode of pixel values. Simple Python programs process a given number of images, recorded consecutively from the same subject. The programs calculate the mode of the pixel values in a given position (a, b). The result is a new image containing in (a, b) the mode of the values. Therefore, the final pixel value corresponds to that read in at least two of the pixels in position (a, b). The application of the program on a set of images obtained by applying salt and pepper noise and GIMP hurl noise with 10-90% standard deviation showed that the mode performs better than averaging with three-eight images. The data suggest that the mode would be more efficient (in the sense of a lower number of recorded images to process to reduce noise below a given limit) for lower number of total noisy pixels and high standard deviation (as impulse noise and salt and pepper noise), while averaging would be more efficient when the number of varying pixels is high, and the standard deviation is low, as in many cases of Gaussian noise affected images. The two methods may be used serially.