Single image deraining using multi-scales context information and attention network

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

Lightweight single image super-resolution with attentive residual refinement network

Recently, deep learning (DL)-based hyperspectral image classification (HSIC) has attracted substantial attention. Many works based on the convolutional neural network (CNN) model have been certificated to be significantly successful for boosting the performance of HSIC. However, most of these methods extract features by using a fixed convolutional kernel and ignore multiscale features of the ground objects of hyperspectral images (HSIs). Although some recent methods have proposed multiscale feature extraction schemes, more computing and storage resources were consumed. Moreover, when using CNN to implement HSI classification, many methods only use the high-level semantic information extracted from the end of the network, ignoring the edge information extracted from shallow layers of the network. To settle the preceding two issues, a novel HSIC method based on hierarchical shrinkage multiscale network and the hierarchical feature fusion is proposed, with which the newly proposed classification framework can fuse features generated by both of multiscale receptive field and multiple levels. Specifically, multidepth and multiscale residual block (MDMSRB) is constructed by superposition dilated convolution to realize multiscale feature extraction. Furthermore, according to the change of feature size in different stages of the neural networks, we design a hierarchical shrinkage multiscale feature extraction network by pruning MDMSRB to reduce the redundancy of network structure. In addition, to make full use of the features extracted in each stage of the network, the proposed network hierarchically integrates low-level edge features and high-level semantic features effectively. Experimental results demonstrate that the proposed method achieves more competitive performance with a limited computational cost than other state-of-the-art methods.

Single image deraining is an important preprocessing task, as rain streaks awfully reduce the image quality and hinder the subsequence outdoor multimedia issues. In this paper, we explore the multi-resolution representation for rain streaks through parallel hierarchical structure and multi-scale feature extraction and fusion, termed Multi-resolution Parallel Aggregation Network (MPA-Net) in end-to-end manner. Specially, considering the significant role of multi-resolution, we employ the first stage to capture the high-resolution features, progressively introduce high-to-low resolution streams to produce more stages, and then connect all stages in parallel. In each stage, Densely Connected Residual (DCR) block is involved to guide the feature extraction. Besides, Cross-Scale Feature Fusion (CSFF) is first introduced to receive and consolidate the correlated features from different scales followed with Squeeze-and-Excitation (SE) blocks, leading to rich the resolution representations. Extensive experiments demonstrate that our method outperforms the recent comparing approaches on the frequent-use synthetic and real-world datasets.KeywordsSingle image derainingMulti-resolution parallelCross-Scale Feature Fusion

Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics. Codes and supplementary material are available at our project webpage: https://xialipku.github.io/RESCAN .

Gait recognition is a distinctive biometric technique that can identify pedestrians by their walking patterns from considerable distances. A critical challenge in gait recognition lies in effectively acquiring discriminative spatial-temporal representations from silhouettes that exhibit invariance to disturbances. In this paper, we present a novel gait recognition network by aggregating features in the spatial-temporal and view domains, which consists of two-path spatial-temporal feature fusion module and view embedding module. Specifically, two-path spatial-temporal feature fusion module firstly utilizes multi-scale feature extraction (MSFE) to enrich the input features with multiple convolution kernels of various sizes. Then, frame-level spatial feature extraction (FLSFE) and multi-scale temporal feature extraction (MSTFE) are parallelly constructed to capture spatial and temporal gait features of different granularities and these features are fused together to obtain muti-scale spatial-temporal features. FLSFE is designed to extract both global and local gait features by employing a specially designed residual operation. Simultaneously, MSTFE is applied to adaptively interact multi-scale temporal features and produce suitable motion representations in temporal domain. Taking into account the view information, we introduce a view embedding module to reduce the impact of differing viewpoints. Through the extensive experimentation over CASIA-B and OU-MVLP datasets, the proposed method has achieved superior performance to the other state-of-the-art gait recognition approaches.

Single image super-resolution using multi-scale feature enhancement attention residual network

With the rapid development of deep learning, no-reference image quality assessment (NR-IQA) based on convolutional neural network (CNN) plays an important role in image processing. Currently, most CNN-based NR-IQA methods focus primarily on the global features of images while ignoring detail-rich local features and channel dependencies. In fact, there are subtle differences in detail between distorted and reference images, as well as differences in the contribution of different channels to IQA. Furthermore, multi-scale feature extraction can be used to fuse the detailed information from images with different resolutions, and the combination of global and local features is critical in extracting image features. As a result, in this paper, a multi-scale residual CNN with an attention mechanism (MsRCANet) is proposed for NR-IQA. Specifically, a multi-scale residual block is first used to extract features from distorted images. Then, the residual learning with active weighted mapping strategy and channel attention mechanism is used to further process image features to obtain more abundant information. Finally, the fusion strategy and full connection layer are used to evaluate image quality. The experimental results on four synthetic databases and three in-the-wild IQA databases, as well as cross-database validation results, show that the proposed method has good generalization ability and can be compared with the most advanced methods.

In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress. However, convolutional neural network models encounter challenges in hyperspectral image classification due to limitations in their receptive fields. Conversely, the global modeling capability of Transformers has garnered attention in hyperspectral image classification. Nevertheless, the high computational cost and inadequate local feature extraction hinder its widespread application. In this study, we propose a novel fusion model of convolutional neural networks and Transformers to enhance performance in hyperspectral image classification, namely the dual-branch multi-granularity convolutional cross-substitution Transformer (DMCCT). The proposed model adopts a dual-branch structure to separately extract spatial and spectral features, thereby mitigating mutual interference and information loss between spectral and spatial data during feature extraction. Moreover, a multi-granularity embedding module is introduced to facilitate multi-scale and multi-level local feature extraction for spatial and spectral information. In particular, the improved convolutional cross-substitution Transformer module effectively integrates convolution and Transformer, reducing the complexity of attention operations and enhancing the accuracy of hyperspectral image classification tasks. Subsequently, the proposed method is evaluated against existing approaches using three classical datasets, namely Pavia University, Kennedy Space Center, and Indian Pines. Experimental results demonstrate the efficacy of the proposed method, achieving significant classification results on these datasets with overall classification accuracies of 98.57%, 97.96%, and 96.59%, respectively. These results establish the superiority of the proposed method in the context of hyperspectral image classification under similar experimental conditions.

Aiming at solving the problems of limited training data, single input information, and limited diagnostic accuracy under the influence of strong background noise in fault diagnosis of rotating machinery, this paper proposes a fault diagnosis method based on the combination of discriminant correlation analysis (DCA) and convolutional neural network (CNN). Firstly, the original vibration signal is divided into several segments in the time domain, and the training data is directly processed by one CNN branch to extract multi-scale time domain features. Simultaneously, the divided data is subjected to discrete wavelet transform (DWT), and processed by another branch of CNN to extract multi-scale time-frequency features. Then, the DCA feature fusion mechanism is adopted to fuse the two-domain features extracted in the parallel branches to improve the model’ detection ability. Finally, the fused features are input into the deep CNN for training and learning to extract new features and output the classification results. Through the experimental analysis of two different types of data, the results show that the proposed method can be used for fault diagnosis of rotating machinery effectively. Compared with the single CNN network, the proposed method combines the multi-domain multi-scale feature extraction module with the DCA feature fusion module to enrich the feature information extraction ability. At the same time, the network performance is improved to get higher fault classification accuracy higher.

The precise and automated diagnosis of apple leaf diseases is essential for maximizing apple yield and advancing agricultural development. Despite the widespread utilization of deep learning techniques, several challenges persist: (1) the presence of small disease spots on apple leaves poses difficulties for models to capture intricate features; (2) the high similarity among different types of apple leaf diseases complicates their differentiation; and (3) images with complex backgrounds often exhibit low contrast, thereby reducing classification accuracy. To tackle these challenges, we propose a three-residual fusion network known as MSCR-FuResNet (Fusion of Multi-scale Feature Extraction and Enhancements of Channels and Residual Blocks Net), which consists of three sub-networks: (1) enhancing detailed feature extraction through multi-scale feature extraction; (2) improving the discrimination of similar features by suppressing insignificant channels and pixels; and (3) increasing low-contrast feature extraction by modifying the activation function and residual blocks. The model was validated with a comprehensive dataset from public repositories, including Plant Village and Baidu Flying Paddle. Various data augmentation techniques were employed to address class imbalance. Experimental results demonstrate that the proposed model outperforms ResNet-50 with an accuracy of 97.27% on the constructed dataset, indicating significant advancements in apple leaf disease recognition.

Modeling accurate text recognizers for scanned Devanagari scripts is a challenging task, primarily due to the ingrained complexities of characters, vowels, conjuncts, and modifiers in the word formation of the script. This paper presents an attention-based convolutional recurrent neural network (CRNN) for improving the text recognition accuracy of the Devanagari script images. The proposed model is an encoder–decoder network with an attention mechanism. The encoder is a convolutional neural network (CNN), which extracts the significant and high-level features from the input images. The encoder also generates the extracted features in an ordered sequence. The decoder uses a bidirectional long short-term (BLSTM) network with an attention mechanism, followed by a connectionist temporal classification (CTC) layer to predict the text in images. The attention mechanism enables the decoder to selectively exploit the high-level features of the encoder congruently. We have trained, validated, and tested the performance of the proposed model on three datasets, viz. publicly available IIIT-H scene image dataset, synthetic dataset, and real dataset curated during this work. We have also compared the proposed model results with work already attempted in the area for the three datasets. We found that the performance of our proposed model outdoes the outcomes obtained from other models on the three datasets.

Convolutional neural networks (CNNs), a kind of feedforward neural network with a deep structure, are one of the representative methods in hyperspectral image (HSI) classification. However, redundant information and interclass interference are common and challenging problems in HSI classification. In addition, if the spectral and spatial information is not properly extracted and analyzed, it will affect the classification performance of the network to a great extent. Aiming at these issues, this article proposes an HSI classification method based on an adaptive hash attention mechanism and a lower triangular network (AHA-LT). First, the attention mechanism is introduced in the preprocessing stage, which is composed of the spectral attention module and the adaptive hash spatial attention module in series. Then, the data processed by the attention mechanism are introduced into the lower triangular network (LTNet) to obtain the fused high-dimensional semantic features. Finally, we compress the features and obtain the output classification results through several fully connected layers. Among them, LTNet is composed of 2-D–3-D CNN and multiscale features. The network integrates the characteristics of multibranch, feature fusion, feature compression, and skip connections. Extensive experiments on four widely used HSI data sets show that the proposed method can obtain a great improvement in performance compared with the existing methods.

To enhance the intelligence of machining processes, accurate recognition of tool wear states has become a key issue in the manufacturing field. However, due to the non-stationary and high-dimensional nature of cutting signals, traditional methods face significant challenges in feature extraction and state classification. In the context of cutting processes, challenges such as difficulty in identifying tool wear states and the complex composition of monitoring information features persist. To address these issues, this paper proposes a deep learning model that integrates multi-scale feature extraction with a residual connection network (Multi-scale ResNet). Specifically, cutting vibration signals are processes using continuous wavelet transform (CWT), which enables the conversion of time-frequency information into images. The proposed deep learning model is then used for feature extraction and state identification. The proposed model is validated through cutting experiments conducted on γ -TiAl alloys. Experimental results show that the Multi-scale ResNet model achieves higher recognition accuracy than traditional models such as convolutional neural networks – support vector machines (CNN–SVM), Transformer, and ResNet in the initial and normal wear stages. It effectively mitigates misjudgments associated with initial and normal wear, achieving a prediction accuracy of 93.8 %, a recall rate of 94.2 %, and an F1 score of 94 %. This model offers a novel and effective approach for tool wear state monitoring, contributing to improved cutting processing efficiency and increased intelligence in production.

Since low-dose computed tomography (LDCT) images typically have higher noise that may affect accuracy of disease diagnosis, the objective of this study is to develop and evaluate a new artifact-assisted feature fusion attention (AAFFA) network to extract and reduce image artifact and noise in LDCT images. In AAFFA network, a feature fusion attention block is constructed for local multi-scale artifact feature extraction and progressive fusion from coarse to fine. A multi-level fusion architecture based on skip connection and attention modules is also introduced for artifact feature extraction. Specifically, long-range skip connections are used to enhance and fuse artifact features with different depth levels. Then, the fused shallower features enter channel attention for better extraction of artifact features, and the fused deeper features are sent into pixel attention for focusing on the artifact pixel information. Besides, an artifact channel is designed to provide rich artifact features and guide the extraction of noise and artifact features. The AAPM LDCT Challenge dataset is used to train and test the network. The performance is evaluated by using both visual observation and quantitative metrics including peak signal-noise-ratio (PSNR), structural similarity index (SSIM) and visual information fidelity (VIF). Using AAFFA network improves the averaged PSNR/SSIM/VIF values of AAPM LDCT images from 43.4961, 0.9595, 0.3926 to 48.2513, 0.9859, 0.4589, respectively. The proposed AAFFA network is able to effectively reduce noise and artifacts while preserving object edges. Assessment of visual quality and quantitative index demonstrates the significant improvement compared with other image denoising methods.