Neural SKCS for efficient noise reduction and content preserving

Abstract

Images are often corrupted by random variations in intensity, illumination or have poor contrast and can't be used directly. Several studies have expressed the need to reduce noise and to improve the visual quality of the image. For this purpose, several mathematical tools have been developed such as image filtering by a convolution filter, such as the kernel with compact support (KCS) which has been recently proposed by Remaki and Cheriet [1] and it's version separable (SKCS) 10]. The effectiveness of the SKCS filter in the smoothing operation depends on the value of the scale parameter. Moreover, if the scale parameter is increased, the image is blurred and details and borders are removed. This disadvantage is related to the static nature of the KCS kernel. In this paper we propose a dynamic and adaptive SKCS filter based on neural networks. The scale parameters involved in the filtering process are calculated in real time and supervised by the neural network. The filter scale varies continuously in order to detect and clean noisy areas of the image. To assess the developed theory, an application of filtering noisy images is presented, including a qualitative comparison between the result obtained by the static SKCS and the adaptive SKCS kernel proposed.