Building a smart dynamic kernel with compact support based on deep neural network for efficient X-ray image denoising

Gaussian filtering is a successful computer operation vision to reduce noise and calculate the gradient intensity change of an image. However, it's well known that in scale space context, the Gaussian kernel has some drawbacks, loss of information caused by the unavoidable Gaussian truncation and the prohibitive processing time due to the mask size. To give a solution to both problems, a new kernel family with compact support and its separable version were presented in the literature. The theoretical study of these kernels shows that the new family kernel is parameterised by a scale parameter and generated in such a way that fine scale structures are successively suppressed when the scale parameter is increased. Moreover, the scale parameter is increased, the image is blurred and details and border are removed. All these disadvantages are related to the static nature of these kernels. In this paper, we propose a smart kernel based on deep neural networks (dnn) to create a dynamic kernel with compact support called DSKCS. The parameter involved in the filtering process is calculated in real time and supervised by deep neural networks. The filter is continuously variable to detect, clean and avoid noisy areas of the image. Extensive experiments show that the proposed kernel can improve the classic kernel and presents a solution for its limitations related to its static nature. Furthermore, different metrics calculated illustrate our approach efficiency. As stated in the filtering performance, which reveals the highest PSNR and NCC with the metrics results (PSNR = 32.18, NCC = 0.95). Also, we recorded more than 0.89 for area under curves of the classification results using DBN-DNN technique.