Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Abstract

Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with 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 color digital images.

Deep Learning Image Processing Enables 40% Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care

“…of data through a complex framework of decision-making nodes known for exemplary performance in image recognition applications, such as the ability to recognize and categorize image features [2]. DL algorithms are applied to an array of computer vision learning tasks in many industries.…”

“…DL models are based on artificial neural networks, most commonly convolutional neural networks (CNN) and variations, in which data transitions through a chain of layers of transformational nodes from input to output, simulating layers of neurons. DL based solutions leverage CNNs to process large volumes of data through a complex framework of decision-making nodes known for exemplary performance in image recognition applications, such as the ability to recognize and categorize image features [ 2 ]. DL algorithms are applied to an array of computer vision learning tasks in many industries.…”

Is large improvement in efficiency of impulsive noise removal in color images still possible?

“…Impulse Detection Convolutional Neural Network [73] is a modification of the Denoising Convolutional Neural Network (DnCNN) [74]. IDCNN consists of a sequence of convolutional layers followed by Rectified Linear Unit [75] and Batch Normalization [76] for feature extraction and sigmoid layer for the noisy pixels detection.…”

“…IDCNN consists of a sequence of convolutional layers followed by Rectified Linear Unit [75] and Batch Normalization [76] for feature extraction and sigmoid layer for the noisy pixels detection. Pretrained model recommended in [73], trained with default parameters summarized in Table 2 was used.…”

Data, Signal and Image Processing and Applications in Sensors

“…Radlak, Malinski and Smolka [ 16 ] presented a switching filtering technique intended for impulsive noise removal using deep learning. The results show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.…”