Residual Channel Attention Research Articles

CT synthesis from MR images using frequency attention conditional generative adversarial network

Magnetic resonance (MR) image-guided radiotherapy is widely used in the treatment planning of malignant tumors, and MR-only radiotherapy, a representative of this technique, requires synthetic computed tomography (sCT) images for effective radiotherapy planning. Convolutional neural networks (CNN) have shown remarkable performance in generating sCT images. However, CNN-based models tend to synthesize more low-frequency components and the pixel-wise loss function usually used to optimize the model can result in blurred images. To address these problems, a frequency attention conditional generative adversarial network (FACGAN) is proposed in this paper. Specifically, a frequency cycle generative model (FCGM) is designed to enhance the inter-mapping between MR and CT and extract more rich tissue structure information. Additionally, a residual frequency channel attention (RFCA) module is proposed and incorporated into the generator to enhance its ability in perceiving the high-frequency image features. Finally, high-frequency loss (HFL) and cycle consistency high-frequency loss (CHFL) are added to the objective function to optimize the model training. The effectiveness of the proposed model is validated on pelvic and brain datasets and compared with state-of-the-art deep learning models. The results show that FACGAN produces higher-quality sCT images while retaining clearer and richer high-frequency texture information.

Exponential Fusion of Interpolated Frames Network (EFIF-Net): Advancing Multi-Frame Image Super-Resolution with Convolutional Neural Networks.

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel accuracy using an affine motion model to capture the camera platform motion. The frames are externally upsampled using single-image interpolation. The interpolated frames are then fused with the custom EWF layer, employing subpixel registration information to give more weight to pixels with less interpolation error. Realistic image acquisition conditions are simulated to generate training and testing datasets with corresponding ground truths. The observation model captures optical degradation from diffraction and detector integration from the sensor. The experimental results demonstrate the efficacy of EFIF-Net using both simulated and real camera data. The real camera results use authentic, unaltered camera data without artificial downsampling or degradation.

RCAU-Net: convolutional networks with residual channel attention for non-uniformity correction

RCAU-Net: convolutional networks with residual channel attention for non-uniformity correction

Residual Channel Attention Fusion Network for Road Extraction Based on Remote Sensing Images and GPS Trajectories

Residual Channel Attention Fusion Network for Road Extraction Based on Remote Sensing Images and GPS Trajectories

RCAU-Net: convolutional networks with residual channel attention for non-uniformity correction

RCAU-Net: convolutional networks with residual channel attention for non-uniformity correction

Super-Resolution Reconstruction of Seismic Images Based on Deep Residual Channel Attention Mechanism

Super-Resolution Reconstruction of Seismic Images Based on Deep Residual Channel Attention Mechanism

An effective hybrid deep neural network for underwater acoustic target recognition

Underwater acoustic target recognition is an important support technology for underwater information detection and signal processing. Due to the high complexity of the marine environment, it is not easy to obtain clear and reliable features from ship-radiated noise, which makes underwater acoustic target recognition difficult. We propose a new hybrid network of transformer networks and convolutional neural networks to alleviate this problem. It consists of three convolution modules and a transformer module. The convolution modules are used to learn the local features of the time-frequency map, and the features are enhanced using the residual structure and channel attention. The transformer module is used to extract the global features of the time-frequency map. The proposed network can obtain global and local features of the ship-radiated noise from the time-frequency map of the raw data, which can improve the accuracy of recognition. Experiments were conducted on DeepShip, a public benchmark dataset of ship-radiated noise, and compared with the current state-of-the-art methods. The results show that the proposed hybrid network achieves the highest recognition accuracy on the DeepShip dataset.

Single image super-resolution via global aware external attention and multi-scale residual channel attention network

Single image super-resolution via global aware external attention and multi-scale residual channel attention network

FuF-Det: An Early Forest Fire Detection Method under Fog

In recent years, frequent forest fires have seriously threatened the earth’s ecosystem and people’s lives and safety. With the development of machine vision and unmanned aerial vehicle (UAVs) technology, UAV monitoring combined with machine vision has become an important development trend in forest fire monitoring. In the early stages, fire shows the characteristics of a small fire target and obvious smoke. However, the presence of fog interference in the forest will reduce the accuracy of fire point location and smoke identification. Therefore, an anchor-free target detection algorithm called FuF-Det based on an encoder–decoder structure is proposed to accurately detect early fire points obscured by fog. The residual efficient channel attention block (RECAB) is designed as a decoder unit to improve the problem of the loss of fire point characteristics under fog caused by upsampling. Moreover, the attention-based adaptive fusion residual module (AAFRM) is used to self-enhance the encoder features, so that the features retain more fire point location information. Finally, coordinate attention (CA) is introduced to the detection head to make the image features correspond to the position information, and improve the accuracy of the algorithm to locate the fire point. The experimental results show that compared with eight mainstream target detection algorithms, FuF-Det has higher average precision and recall as an early forest fire detection method in fog and provides a new solution for the application of machine vision to early forest fire detection.

VSTNet: Robust watermarking scheme based on voxel space transformation for diffusion tensor imaging images

Deep watermarking has been successfully applied to real-value domain (e.g., CT, MRI, DWI) medical images for copyright protection; however, manifold-value domain images, such as diffusion tensor imaging (DTI), have received less academic attention. The non-Euclidean nature of DTI images hinders deep models from generating plausible DTI images because it is difficult to guarantee that each generated DTI image voxel lies on a 3 × 3 symmetric positive definite manifold. In this paper, we propose a robust watermarking scheme based on voxel space transformation for DTI images. First, a voxel space transformation is proposed, which effectively solves the problem of fiber orientation estimation in Riemann networks for DTI images and obtains the eigenvalues and eigenvectors of each voxel using singular value decomposition (SVD) to transform the DTI voxels from the 3 × 3 symmetric positive definite manifold space to Euclidean space. Second, SVD is combined with a deep neural network to extract the high-level features of each voxel eigenvalue in the DTI images and embedding watermarking messages, avoiding the false positive errors problem in traditional watermarking algorithms based on an SVD transform. Finally, using a deep neural network combining multiscale dilated convolution, dense residual connection and channel attention, the algorithm demonstrates high robustness against various intentional or unintentional attacks on DTI images while ensuring the good visual quality and diffusion characteristics of the DTI images embedded with watermarking messages. Experiments show that the watermarking message extraction accuracy reaches 97.8% for Crop (p = 0.7) attacks and 95.4% for Gaussian Blur (k = 7) attacks.

Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency.

In the advancement of medical image super-resolution (SR), the Deep Residual Feature Distillation Channel Attention Network (DRFDCAN) marks a significant step forward. This work presents DRFDCAN, a model that innovates traditional SR approaches by introducing a channel attention block that is tailored for high-frequency features-crucial for the nuanced details in medical diagnostics-while streamlining the network structure for enhanced computational efficiency. DRFDCAN's architecture adopts a residual-within-residual design to facilitate faster inference and reduce memory demands without compromising the integrity of the image reconstruction. This design strategy, combined with an innovative feature extraction method that emphasizes the utility of the initial layer features, allows for improved image clarity and is particularly effective in optimizing the peak signal-to-noise ratio (PSNR). The proposed work redefines efficiency in SR models, outperforming established frameworks like RFDN by improving model compactness and accelerating inference. The meticulous crafting of a feature extractor that effectively captures edge and texture information exemplifies the model's capacity to render detailed images, necessary for accurate medical analysis. The implications of this study are two-fold: it presents a viable solution for deploying SR technology in real-time medical applications, and it sets a precedent for future models that address the delicate balance between computational efficiency and high-fidelity image reconstruction. This balance is paramount in medical applications where the clarity of images can significantly influence diagnostic outcomes. The DRFDCAN model thus stands as a transformative contribution to the field of medical image super-resolution.

Fast Brillouin optical time domain sensing based on two-dimension sparse sampling and deep learning recovery

Brillouin optical time domain sensing (BOTDS) is a technique for acquiring the Brillouin scattering spectrum (BSS) of a fiber under test (FUT) by utilizing multiple frequency scans. However, the time-consuming nature of this process limits its applicability in scenarios that require rapid measurements. In order to enhance measurement efficiency and decrease storage capacity, without compromising measurement accuracy or spatial resolution, we propose a novel fast BOTDS method based on two-dimensional (2D) sparse sampling and residual channel attention network (RCAN). The proposed method employs sparse sampling to measure the BSS signal, which is then accurately restored to the BSS with normal frequency sampling intervals by using deep learning techniques. Temperature and strain information is extracted from the restored BSS, facilitating rapid and precise measurements. The effectiveness of the proposed method has been verified through simulation and experiments. In a proof-of-concept experiment, we employed a 3000 m bend-insensitive fiber as the sensing fiber and reduced the sampling frequency by a factor of 10. Our proposed method successfully recovered the Brillouin scattering spectrum signal, resulting in a temperature measurement uncertainty of 0.25 °C. Moreover, the measurement accuracy and spatial resolution almost remained unaffected, surpassing that of interpolation-based approaches.

Frequency and content dual stream network for image dehazing

Image dehazing can improve image clarity and visual effect, which plays a pivotal role in many computer vision tasks. Existing dehazing methods are mostly based on a single feature stream and tend to ignore the low-frequency characteristics of haze. In this paper, we propose a dual stream network for image dehazing. To enhance the edge information and texture detail of the image, we construct a frequency stream based on attention octave convolution. We decompose the features into high and low-frequency branches in the frequency stream to obtain different structural information. By adding a residual channel attention block, the attention octave convolution can extract frequency features more efficiently and effectively. Due to the lower resolution of low-frequency features in the frequency stream, the frequency stream features alone are insufficient for recovering the overall content of the image. Therefore, a content stream was added to compensate for the information lost in the frequency stream. By fusing the outputs of two feature streams, the network achieves an enhanced dehazing performance. The results show that our method is superior to other state-of-the-art algorithms in quantitative evaluation and visual impact.

CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism.

Compressed sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically generative adversarial networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning reconstruction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study, we present a novel GAN-based model that delivers superior performance without the model complexity escalating. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby focusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies affirmed the efficacy of the modified modules within the network. Incorporating DR networks and CAM elevated the peak signal-to-noise ratios (PSNR) of the reconstructed images by about 1.2 and 0.8 dB, respectively, on average, even at 10× CS acceleration. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent stability but also outperforming most of the compared networks in terms of PSNR and SSIM. When compared with U-net, DR-CAM-GAN's average gains in SSIM and PSNR were 14% and 15%, respectively. Its MSE was reduced by a factor that ranged from two to seven. The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Deep recurrent residual channel attention network for single image super-resolution

Deep recurrent residual channel attention network for single image super-resolution

Fourier Ptychographic Microscopic Reconstruction Method Based on Residual Hybrid Attention Network.

Fourier ptychographic microscopy (FPM) is a novel technique for computing microimaging that allows imaging of samples such as pathology sections. However, due to the influence of systematic errors and noise, the quality of reconstructed images using FPM is often poor, and the reconstruction efficiency is low. In this paper, a hybrid attention network that combines spatial attention mechanisms with channel attention mechanisms into FPM reconstruction is introduced. Spatial attention can extract fine spatial features and reduce redundant features while, combined with residual channel attention, it adaptively readjusts the hierarchical features to achieve the conversion of low-resolution complex amplitude images to high-resolution ones. The high-resolution images generated by this method can be applied to medical cell recognition, segmentation, classification, and other related studies, providing a better foundation for relevant research.

Channel and temporal-frequency attention UNet for monaural speech enhancement

The presence of noise and reverberation significantly impedes speech clarity and intelligibility. To mitigate these effects, numerous deep learning-based network models have been proposed for speech enhancement tasks aimed at improving speech quality. In this study, we propose a monaural speech enhancement model called the channel and temporal-frequency attention UNet (CTFUNet). CTFUNet takes the noisy spectrum as input and produces a complex ideal ratio mask (cIRM) as output. To improve the speech enhancement performance of CTFUNet, we employ multi-scale temporal-frequency processing to extract input speech spectrum features. We also utilize multi-conv head channel attention and residual channel attention to capture temporal-frequency and channel features. Moreover, we introduce the channel temporal-frequency skip connection to alleviate information loss between down-sampling and up-sampling. On the blind test set of the first deep noise suppression challenge, our proposed CTFUNet has better denoising performance than the champion models and the latest models. Furthermore, our model outperforms recent models such as Uformar and MTFAA in both denoising and dereverberation performance.

Pixel-Wise Grasp Detection via Twin Deconvolution and Multi-Dimensional Attention

The grasp detection is crucial to high-quality robotic grasping. Typically, the mainstream encoder-decoder regression solution is attractive due to its high accuracy and efficiency, however, it is still challenging to solve the checkerboard artifacts from the uneven overlap of convolution results in decoder, and features from the encoder also need to be further refined. In this paper, a novel pixel-wise grasp detection network is proposed, which is composed of an encoder, a multi-dimensional attention bottleneck, and a decoder based on twin deconvolution. The proposed decoder introduces a twin branch upon the original transposed convolution branch. Through the overlap degree matrix provided by the twin branch, the original branch is re-weighted and then the checkerboard artifacts of the original branch are eliminated. Besides, to deeply explore the intrinsic relationship of features and strengthen feature discrimination, residual multi-head self-attention, cross-amplitude attention, and channel attention are integrated together. As a result, adaptive feature refinement is achieved. The effectiveness of the proposed method is verified by experiments.

Design of a Wind Power Forecasting System Based on Deep Learning

In recent years, wind energy, as a ubiquitous, easy-to-capture cost-effective clean energy, is accounting for a sharp increase in the installed capacity of China’s new energy grid. However, its stochastic volatility has always brought challenges to the generation scheduling of wind farms. To better optimize the energy management level of wind farms and improve the stability of wind power grid connection, this paper proposes a design of a wind power forecasting system based on deep learning. Our system architecture mainly includes data pre-processing, power prediction, and data application modules. The data pre-processing module will correct the numerical weather forecast (NWP) updated twice daily to get the wind turbine generator (WTG) hub height weather forecast and resample the SCADA data in a 15-minute time resolution. The power prediction module will periodically perform the ultra-short-term and short-term forecasting results and record them in the database. On one hand, the data application module will present the power prediction results and SCADA data to the web page for display. On the other hand, it will provide data to the energy dispatching department for reference. The experiment shows the effect of using a residual channel attention network (RCAN) to correct the NWP and the influence of two different RNN cores, including GRU and LSTM, on the prediction of ultra-short-term wind power results of the DeepAR model. The experimental results show that the proposed RCAN can effectively correct the results of the numerical weather forecast to a single WTG, and the DeepAR model using the GRU core can achieve better performance in the test set than the DeepAR model using the LSTM core. Thus, we choose GRU as the core of the model of our power prediction module.

Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills

Effective tool wear monitoring (TWM) is crucial for accurately assessing the degree of tool wear, guiding tool replacement during actual cutting processes, ensuring stable machine operation, and improving workpiece processing quality. With the arrival of the era of Big data, more and more data-driven monitoring methods are used for TWM problems, but it also exposes the problems of over reliance on artificial feature extraction and selection, low robustness of the actual industrial environment and poor generalization of different machining processes. To solve these problems, this paper proposes a multi-scale one-dimensional convolution (MODC-MMFL) end-to-end TWM integrated network model based on multi-model fusion learning (MMFL) skills. Firstly, multi-scale local features of multi-sensor signals are adaptively extracted by multi-scale one-dimensional convolution (MODC) network, to realize multi-feature fusion. Then, using MMFL skills, the MMFL network is composed of deep attention temporal convolutional network (DATCN) and stacked bidirectional gate recurrent unit network (SBIGRU)， parallel learning time series features related to tool wear characteristics，and use a fusion layer to fuse these learned features, in which residual channel attention mechanism (RCAM) is used to improve network performance in DATCN network. Finally, the predicted tool wear value is output by fully connected regression network (FCR). In addition, this paper uses the PHM tool wear dataset to conduct experimental study on the proposed model, first verifying the effectiveness of the proposed model. Then, ablation experiments were conducted to investigate the impact of hyper-parameters on the predictive performance of the model. The model was enhanced through hyper-parameter tuning, and a generalized enhanced model was established. The experimental results showed that the enhanced model had better predictive performance compared to ordinary models. Finally, Gaussian noise is added to the original signal of the PHM tool wear dataset to simulate the high noise signal of the actual industrial environment. The noise signal is used to carry out experimental study on the enhanced model. The experimental results show that the enhanced model still has good prediction performance in the high noise environment and has high robustness to the actual industrial environment. After the above research, this paper uses the NASA tool wear dataset to conduct experimental study on the proposed model. The experimental results show that the proposed model has good predictive performance for different machining processes, verifying the generalizability of the proposed model for different machining processes. In summary, the model proposed in this paper can accurately predict tool wear values based on processing monitoring information, and has good predictive performance, anti-interference ability, and environmental adaptability, making it very suitable for practical industrial applications.