Suppression of the Block Effect while Enhancing Dark Images Using Asumu Derivative

Abstract

Image contrast enhancement is one of the most important tasks in the field of image digital processing. With the help of computers and programming languages, many mathematical methods have been implemented to improve the visual quality of an image. However, the majority of these methods have a major drawback: they tend to make the image look blocked. This effect is visually unpleasant and is likely to cause a computer vision system to falsely recognize as edges the boundaries of different blocks that belong to the same smooth area in the original image. To avoid the block effect while enhancing the quality of dark images, we propose a new fractional differential mask based on the Asumu fractional derivative. Experiments with dark natural images show that, even though our method does not have a better performance in statistical quality, it achieves a solution to the problem that was the motivation to write this paper, suppressing efficiently the block effect while improving the visual qualities of dark images.