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Image denoising using channel attention residual enhanced Swin Transformer
Transformers have achieved remarkable results in high-level vision tasks, but their application in low-level computer vision tasks such as image...
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Single Image Deraining Using Residual Channel Attention Networks
Image deraining is a highly ill-posed problem. Although significant progress has been made due to the use of deep convolutional neural networks, this...
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Deep recurrent residual channel attention network for single image super-resolution
The models based on convolutional neural network have achieved excellent results in image super-resolution by acquiring prior knowledge from a large...
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Light field angular super resolution based on residual channel attention and classification up-sampling
Current light field angular super resolution algorithms generate coarse viewpoint images due to their low learning ability and equally upsample all...
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Face Recognition Based on Improved Residual Network and Channel Attention
AbstractWith the continuous development of deep learning, convolutional neural networks have achieved good results in the field of face recognition....
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Residual guided coordinate attention for selection channel aware image steganalysis
According to the embedding probability used in modern content adaptive steganography, some selection channel aware (SCA) methods have been proposed...
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A deep feature fusion network using residual channel shuffled attention for cassava leaf disease detection
Cassava is a significant source of carbohydrates for tropical populations. However, diseases caused by agents such as bacteria, viruses, fungi, and...
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NResNet: nested residual network based on channel and frequency domain attention mechanism for speaker verification in classroom
With the development of deep learning technology, the pattern of artificial intelligence in education has attracted more and more attention. However,...
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HTC-Net: Hashimoto’s thyroiditis ultrasound image classification model based on residual network reinforced by channel attention mechanism
Convolutional neural network (CNN) is efficient in extracting and aggregating local features in the spatial dimension of the images. However,...
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Residual Feature Distillation Channel Spatial Attention Network for ISP on Smartphone
With the increasing popularity of mobile photography, more and more attention is being paid to image signal processing(ISP) algorithms used to...
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A novel 3D shape recognition method based on double-channel attention residual network
Learning 3D features by deep networks has achieved a successful performance up to now. However, data imbalance and low-resolution voxels still remain...
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LCRCA: image super-resolution using lightweight concatenated residual channel attention networks
Images that are more similar to the original high-resolution images can be generated by deep neural network-based super-resolution methods than the...
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Attention-driven residual-dense network for no-reference image quality assessment
With the rapid development of deep learning, convolutional neural networks have been applied to no-reference image quality assessment (NR-IQA), but...
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Cascaded refinement residual attention network for image outpainting
The image outpainting based on deep learning shows good performance and has a wide range of applications in many fields. The previous image...
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GRAN: ghost residual attention network for single image super resolution
Recently, many works have designed wider and deeper networks to achieve higher image super-resolution performance. Despite their outstanding...
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Residual UNet with spatial and channel attention for automatic magnetic resonance image segmentation of rectal cancer
The precise segmentation of rectal tumors is a key step in the diagnosis and treatment of rectal cancer. This paper aims to study the automatic...
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Attention-based multi-scale recursive residual network for low-light image enhancement
Aiming at the problems of color distortion, low image processing efficiency, rich context information, spatial information imbalance in the current...
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Leaf disease recognition based on channel information attention network
Aiming at the problem of the variety of plant leaf diseases and how to extract effective features, an attention network model fused with channel...
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Multitask Learning with Multiscale Residual Attention for Brain Tumor Segmentation and Classification
Automatic segmentation and classification of brain tumors are of great importance to clinical treatment. However, they are challenging due to the...
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Wide Activation Fourier Channel Attention Network for Super-Resolution
Attention mechanisms, especially channel attention, have been widely used in a wide range of tasks in computer vision. More recently, researchers...