1 research outputs found
Deep Learning based Switching Filter for Impulsive Noise Removal in Color Images
Noise reduction is one the most important and still active research topic in
low-level image processing due to its high impact on object detection and scene
understanding for computer vision systems. Recently, we can observe a
substantial increase of interest in the application of deep learning algorithms
in many computer vision problems due to its impressive capability of automatic
feature extraction and classification. These methods have been also
successfully applied in image denoising, significantly improving the
performance, but most of the proposed approaches were designed for Gaussian
noise suppression. In this paper, we present a switching filtering design
intended for impulsive noise removal using deep learning. In the proposed
method, the impulses are identified using a novel deep neural network
architecture and noisy pixels are restored using the fast adaptive mean filter.
The performed experiments show that the proposed approach is superior to the
state-of-the-art filters designed for impulsive noise removal in digital color
images