Deep Learning based Switching Filter for Impulsive Noise Removal in Color Images
This work addresses a specific problem in low-level image processing for computer vision systems, offering an incremental improvement by applying deep learning to a domain previously focused on Gaussian noise.
The paper tackles impulsive noise removal in color images by proposing a switching filter that uses a novel deep neural network to identify impulses and a fast adaptive mean filter for restoration, achieving superior performance compared to state-of-the-art methods.
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.