A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Abstract

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

Cross-Modal Guidance Assisted Hierarchical Learning Based Siamese Network for MR Image Denoising

“…The conventional deep learning models for image denoising are trained to learn the mapping from noisy image Y to non-noisy image X [41,53]. However, in cross-modalityguided denoising methods, the model incorporates an additional multi-modal image G to learn complementary information and facilitate the learning process.…”

“…Few research works presented for medical image denoising [23,40] show improved performance over their single image denoising counterparts. Single image denoising approaches have an intrinsic limitation where the corrupted information in the original image is only hallucinated during the reconstruction process [41]. Consequently, these approaches over smooth certain critical structures in the image at the expense of removing noise [42].…”

Efficient Iterative Regularization Method for Total Variation-Based Image Restoration

“…If we ignore the specific form of ϕ(x), superior denoisers such as non-local means filter [21], block-matching and 3D filtering (BM3D) [22], bilateral filter [23], and adversarial Gaussian denoiser [24], can be adopted for solving this denoising subproblem. Moreover, with the rapid development of deep learning technology in image denoising, super-resolution reconstruction, object detection and control [25,26], deep learning methods [27][28][29][30] using clean-noisy image pairs have been widely exploited in the design of denoisers. Multi-layer perceptron was adopted for image restoration in [27] while various convolutional neural network (CNN) and generative adversarial networks methods have been used to design specific denoisers [28][29][30].…”

“…Moreover, with the rapid development of deep learning technology in image denoising, super-resolution reconstruction, object detection and control [25,26], deep learning methods [27][28][29][30] using clean-noisy image pairs have been widely exploited in the design of denoisers. Multi-layer perceptron was adopted for image restoration in [27] while various convolutional neural network (CNN) and generative adversarial networks methods have been used to design specific denoisers [28][29][30]. It is well known that neural network methods are limited in computing speed and high requirements of hardware, with no universal adaptation for the applications that require simplicity and rapidity.…”

Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

“…Hence, it is effective to make use of machine learning algorithms to achieve an automated MI diagnosis [13][14][15]. In the past few years, deep learning (DL) methods, including convolutional neural networks (CNN), recurrent neural networks, restricted Boltzmann machines [16], autoencoder, and generative adversarial networks are proposed [17][18][19][20]. These network architectures or learning methods are used for ECG classification, denoising, reconstruction, annotation, data compression, data generation, and data synthesis purposes.…”