Contrast enhancement in grayscale digital images applying atomic functions in fuzzy logic

In digital image processing, images must have the best quality possible, so, they can provide the desired information without any loss. Digital image pre-processing allows enhancing, removing and recovering certain characteristics that permit to obtain better image quality for following tasks: segmentation, compression and restoration. This study focuses in contrast enhancement of an image. An extension of the method of contrast enhancement based on fuzzy set theory is proposed. Several atomic functions are employed as membership functions and also new border values of these are introduced. Designed scheme has confirmed better enhancement in comparison with the conventional method, demonstrated by the quality indexes: decreasing of the contrast and luminance values in SSIM measure, better performances in the fuzzy logarithmic entropy and the Kaufmann's fuzziness indexes, finally a visible enhancement is viewed subjectively better via human visual system.