Image filtering by dynamic KCS

Through the past decades, several studies have expressed the need to improve image quality to reduce the processing time. For this purpose, several mathematical tools have been developed such as image filtering by a convolution filter, such as the Gaussian filter or kernel with compact support (KCS) which has been recently proposed by Remaki and Cheriet [1] .The effectiveness of this 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 KCS 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 image s is presented, including a qualitative comparison between the result obtained by the static KCS and the adaptive KCS kernel proposed.