MSFSA-GAN: Multi-Scale Fusion Self Attention Generative Adversarial Network for Single Image Deraining

Wang, Xue; Cheng, Huanxin; Sheng-yi, Sun; Ze-qin, Jiang; Chen, Kai; Li, Cheng

doi:10.1109/access.2022.3162224

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Yang et al [37] proposed a generative adversarial network based on a hybrid attention mechanism for data augmentation, which improves the detail ability of GANs to generate images by relating the remote features in images and further increases the stability of training. Wang et al [38] proposed a multi-scale fusion self-attention GAN to solve the problems of the image quality and accuracy of visual processing algorithms affected by bad weather. By introducing self-attention and using different scales to extract the input features of the rain line, the network pays more attention to the extracted features of different scales.…”

Section: Ammentioning

confidence: 99%

DCGAN-Based Image Data Augmentation in Rawhide Stick Products’ Defect Detection

Ding,

Guo,

Chen

et al. 2024

Electronics

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The online detection of surface defects in irregularly shaped products such as rawhide sticks, a kind of pet food, is still a challenge for the food industry. Developing deep learning-based detection algorithms requires a diverse defect database, which is crucial for artificial intelligence applications. Acquiring a sufficient amount of realistic defect data is challenging, especially during the beginning of product production, due to the occasional nature of defects and the associated costs. Herein, we present a novel image data augmentation method, which is used to generate a sufficient number of defect images. A Deep Convolution Generation Adversarial Network (DCGAN) model based on a Residual Block (ResB) and Hybrid Attention Mechanism (HAM) is proposed to generate massive defect images for the training of deep learning models. Based on a DCGAN, a ResB and a HAM are utilized as the generator and discriminator in a deep learning model. The Wasserstein distance with a gradient penalty is used to calculate the loss function so as to update the model training parameters and improve the quality of the generated image and the stability of the model by extracting deep image features and strengthening the important feature information. The approach is validated by generating enhanced defect image data and conducting a comparison with other methods, such as a DCGAN and WGAN-GP, on a rawhide stick experimental dataset.

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Section: Ammentioning

confidence: 99%

DCGAN-Based Image Data Augmentation in Rawhide Stick Products’ Defect Detection

Ding,

Guo,

Chen

et al. 2024

Electronics

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Infrared and visible image fusion method based on hierarchical attention mechanism

Li,

Yan,

Luo

2024

J. Electron. Imag.

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Currently, most feature extraction methods lack generality and require specific feature extraction methods for images from different sensors, particularly in the detection of faults in power equipment. However, the challenge lies in how to restore realistic texture details while correcting the color distortion. To address this issue, we propose an innovative infrared-visible light image fusion technique that combines hierarchical attention modules and collaborative refinement modules to facilitate feature fusion by jointly preserving intricate details and correcting lighting conditions. The hierarchical attention module aims to provide two different attention weight maps, which help select the most salient information from the source images in the infrared and visible light domains. This ultimately produces intermediate fusion results with comprehensive and complementary data. The collaborative refinement module consists of an edge enhancement network and a lighting correction network, which works together to enhance edge details and correct color differences. Experimental results validate the effectiveness of this method in successfully fusing the two types of images. The results demonstrate that, compared with several mainstream fusion methods, this method exhibits significant advantages in publicly available datasets and scenarios, such as power equipment detection.

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A Deep Learning-Based Two-Branch Generative Adversarial Network for Image De-Raining

Zhao,

Long,

Zhong

2024

Sensors

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Raindrops can scatter and absorb light, causing images to become blurry or distorted. To improve image quality by reducing the impact of raindrops, this paper proposes a novel generative adversarial network for image de-raining. The network comprises two parts: a generative network and an adversarial network. The generative network performs image de-raining. The adversarial network determines whether the input image is rain-free or de-rained. The generative network comprises two branches: the A branch, which follows a traditional convolutional network structure, and the U branch, which utilizes a U-Net architecture. The A branch includes a multi-scale module for extracting information at different scales and a residual attention module to reduce redundant information interference. The U branch contains an encoder module designed to address the loss of details and local information caused by conventional down-sampling. To improve the performance of the generative network in image de-raining, this paper employs a relative discriminator incorporating a mean squared error loss. This discriminator better measures the differences between rainy and rain-free images while effectively preventing the occurrence of gradient vanishing. Finally, this study performs visual and quantitative comparisons of the proposed method and existing methods on three established rain image datasets. In the quantitative experiments, the proposed method outperforms existing methods regarding PSNR, SSIM, and VIF metrics. Specifically, our method achieves an average PSNR, SSIM, and VIF of approximately 5%, 3%, and 4% higher than the MFAA-GAN method, respectively. These results indicate that the de-rained images generated via the proposed method are closer to rain-free images.

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MSFSA-GAN: Multi-Scale Fusion Self Attention Generative Adversarial Network for Single Image Deraining

Cited by 3 publications

References 30 publications

DCGAN-Based Image Data Augmentation in Rawhide Stick Products’ Defect Detection

DCGAN-Based Image Data Augmentation in Rawhide Stick Products’ Defect Detection

Infrared and visible image fusion method based on hierarchical attention mechanism

A Deep Learning-Based Two-Branch Generative Adversarial Network for Image De-Raining

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