Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining

Wang, Jyun-Guo; Wu, Cheng-Shiuan

doi:10.3390/app13042709

Cited by 4 publications

(3 citation statements)

References 69 publications

(72 reference statements)

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“…In [54], [55], an end-to-end recurrent multilevel residual and global attention network and multi-scale context information and attention network are proposed. In [56] multi-scale aggregation residual channel attention fusion network (MARCAFNet) is proposed. A lightweight semisupervised network (LSNet) for single image deraining is proposed by [57].…”

Section: B Data-driven Image Deraining Methodsmentioning

confidence: 99%

LPN-IDD: A Lightweight Pyramid Network for Image Deraining and Detection

Babar,

Yaseen,

Al-Shamayleh

et al. 2024

IEEE Access

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One of the challenges in image processing involves single-image deraining (SID). Existing methods do not exploit images at multi-scales and often overlook spatial and channel information. These methods fail to account for different rain conditions, leading to challenges in effectively removing a wide range of rain streak patterns, such as diverse directional or dense streaks. Moreover, they often result in the loss of fine texture details or a blurred background in the process of eliminating rain streaks from images captured in heavy rain. Mostly state-of-the-art (SOTA) deraining models achieve higher performance in removing rain from rainy images but at the expense of a high number of parameters, which results in computational complexity and memory requirements. Furthermore, they also do not consider high-level visioned evaluation metrics to perform deep evaluations of the proposed models. In this paper, a simple lightweight network with few parameters and relatively shallow depth is proposed by fusing the traditional Gaussian-Laplacian pyramid technique with the attention module. We propose a novel attention-based lightweight pyramid network for image deraining and detection (LPN-IDD) to achieve better deraining performance. The proposed model includes a dual attention module integrated with the Gaussian-Laplacian pyramids network. In LPN-IDD, different levels of laplacian pyramids can extract multi-scale features to adapt to different shapes and types of rain streaks. Residual and recursive blocks are used in each subnetwork along with dual attention blocks to resist the occlusion or texture feature while suppressing unnecessary features. Extensive experimentation performed on the SID synthetic and real-world datasets demonstrate the effectiveness of the proposed model in image deraining tasks. For deep evaluation of the proposed methodology, object detection models including faster-RCNN, YOLO-V3, YOLO-V7 are used along with full reference evaluation metrics.

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Section: B Data-driven Image Deraining Methodsmentioning

confidence: 99%

LPN-IDD: A Lightweight Pyramid Network for Image Deraining and Detection

Babar,

Yaseen,

Al-Shamayleh

et al. 2024

IEEE Access

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show abstract

“…For example, interactive attention can learn to find key features and salient parts from the input data to achieve better task effects [32], and multi-head attention [33] runs multiple attention mechanisms on the same input data and merges the results. The channel attention [34,35,36,37] that is mainly studied in this paper, can find the specific data in complex data, and improve the accuracy and efficiency of the model by learning how to adjust the weight of each channel in the input data.…”

Section: B Attention Mechanismmentioning

confidence: 99%

3D Graph Convolutional Feature Selection and Dense Pre-Estimation for Skeleton Action Recognition

Zhang,

Yang,

Miao

et al. 2024

IEEE Access

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Action recognition plays an important role in promoting various applications in healthcare and smart education. However, unclear target actions, similar actions, and occluded characters may be encountered in some special scenarios. To solve the issues, a 3D Graph Convolutional Feature Selection and Dense Pre-estimation for Skeleton Action Recognition (3D-GSD) method is proposed to analyze and recognize the motion trajectory of the human skeleton. First, 3DSKNet is designed to adaptively learn and select important features in the skeleton sequence to identify skeleton parts of different importance more accurately according to the size of the input image resolution. It will help to better focus on key skeletal parts, improving the accuracy and robustness of bone recognition. Then, the DensePose algorithm is used to detect the complex key points of the human body posture and optimize the accuracy and interpretability of action recognition for different key points, key channels, and key-frames of the action. The proposed method achieves the best performance on the NTU RGB+D 60, NTU RGB+D 120 datasets, and Kinetics SKeletion 400 datasets, with an improvement of 0.02% 0.06%, and 0.1% in accuracy compared to the state-of-the-art methods.

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“…Nasnetmobile is a pretrained neural network model [34] that has been trained using transfer learning. Transfer learning is a method that involves the knowledge transfer learned from a pretrained model to a new task.…”

Section: Feature Extraction Using Proposed Cnnmentioning

confidence: 99%

A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

Haseeb,

Khan,

Alhaisoni

et al. 2023

CMC

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Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the leastimportant features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks. This process aims to improve the learning capability of deep models and lessen the number of parameters. Besides, this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder phase. The research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization algorithm. These features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network classifier. The experiment worked on the HyperKvasir dataset, which consists of 23 stomach-infected classes. At last, the proposed method obtained an improved accuracy of 93.90% on this dataset. Comparison is also conducted with some recent techniques and shows that the proposed method's accuracy is improved.

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Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining

Cited by 4 publications

References 69 publications

LPN-IDD: A Lightweight Pyramid Network for Image Deraining and Detection

LPN-IDD: A Lightweight Pyramid Network for Image Deraining and Detection

3D Graph Convolutional Feature Selection and Dense Pre-Estimation for Skeleton Action Recognition

A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

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