Coastal zone image dehazing network based on feature fusion and adversarial training

2020 ◽  
Author(s):  
Xiaoyu Wu ◽  
Gong Chen ◽  
Xiangqian Ding ◽  
Kezhen Xie ◽  
Guanqun wei
Keyword(s):  
Coastal Zone ◽  
Feature Fusion ◽  
Image Dehazing ◽  
Adversarial Training

scholarly journals Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3922
Author(s):  
Sheeba Lal ◽  
Saeed Ur Rehman ◽  
Jamal Hussain Shah ◽  
Talha Meraj ◽  
Hafiz Tayyab Rauf ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Feature Fusion ◽  
Speckle Noise ◽  
Fundus Images ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Adversarial Attack ◽  
Retinal Fundus Images ◽  
Attacks And Defenses ◽  
Retinal Fundus

Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.

scholarly journals HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ling Zhu ◽  
Hongqing Zhu ◽  
Suyi Yang ◽  
Pengyu Wang ◽  
Yang Yu
Keyword(s):  
High Resolution ◽  
Loss Function ◽  
Pulmonary Nodule ◽  
Feature Fusion ◽  
Back Propagation ◽  
Pulmonary Nodules ◽  
Improve Patient Survival ◽  
Multi Scale ◽  
Training Scheme ◽  
Adversarial Training

AbstractAccurate segmentation and classification of pulmonary nodules are of great significance to early detection and diagnosis of lung diseases, which can reduce the risk of developing lung cancer and improve patient survival rate. In this paper, we propose an effective network for pulmonary nodule segmentation and classification at one time based on adversarial training scheme. The segmentation network consists of a High-Resolution network with Multi-scale Progressive Fusion (HR-MPF) and a proposed Progressive Decoding Module (PDM) recovering final pixel-wise prediction results. Specifically, the proposed HR-MPF firstly incorporates boosted module to High-Resolution Network (HRNet) in a progressive feature fusion manner. In this case, feature communication is augmented among all levels in this high-resolution network. Then, downstream classification module would identify benign and malignant pulmonary nodules based on feature map from PDM. In the adversarial training scheme, a discriminator is set to optimize HR-MPF and PDM through back propagation. Meanwhile, a reasonably designed multi-task loss function optimizes performance of segmentation and classification overall. To improve the accuracy of boundary prediction crucial to nodule segmentation, a boundary consistency constraint is designed and incorporated in the segmentation loss function. Experiments on publicly available LUNA16 dataset show that the framework outperforms relevant advanced methods in quantitative evaluation and visual perception.

scholarly journals A new end-to-end image dehazing algorithm based on residual attention mechanism

2021 ◽  
Vol 39 (4) ◽  
pp. 901-908
Author(s):  
Zhenjian Yang ◽  
Jiamei Shang ◽  
Zhongwei Zhang ◽  
Yan Zhang ◽  
Shudong Liu
Keyword(s):  
Feature Extraction ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Scale Feature ◽  
Multi Scale ◽  
Color Distortion ◽  
End To End ◽  
Density Image

Traditional image dehazing algorithms based on prior knowledge and deep learning rely on the atmospheric scattering model and are easy to cause color distortion and incomplete dehazing. To solve these problems, an end-to-end image dehazing algorithm based on residual attention mechanism is proposed in this paper. The network includes four modules: encoder, multi-scale feature extraction, feature fusion and decoder. The encoder module encodes the input haze image into feature map, which is convenient for subsequent feature extraction and reduces memory consumption; the multi-scale feature extraction module includes residual smoothed dilated convolution module, residual block and efficient channel attention, which can expand the receptive field and extract different scale features by filtering and weighting; the feature fusion module with efficient channel attention adjusts the channel weight dynamically, acquires rich context information and suppresses redundant information so as to enhance the ability to extract haze density image of the network; finally, the encoder module maps the fused feature nonlinearly to obtain the haze density image and then restores the haze free image. The qualitative and quantitative tests based on SOTS test set and natural haze images show good objective and subjective evaluation results. This algorithm improves the problems of color distortion and incomplete dehazing effectively.

scholarly journals FFA-Net: Feature Fusion Attention Network for Single Image Dehazing

2020 ◽  
Vol 34 (07) ◽  
pp. 11908-11915 ◽  
Author(s):  
Xu Qin ◽  
Zhilin Wang ◽  
Yuanchao Bai ◽  
Xiaodong Xie ◽  
Huizhu Jia
Keyword(s):  
Network Architecture ◽  
Feature Fusion ◽  
Low Frequency ◽  
Basic Block ◽  
Single Image ◽  
Image Dehazing ◽  
Residual Learning ◽  
Feature Attention ◽  
Types Of Information ◽  
Single Image Dehazing

In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key components:1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels. FA treats different features and pixels unequally, which provides additional flexibility in dealing with different types of information, expanding the representational ability of CNNs. 2) A basic block structure consists of Local Residual Learning and Feature Attention, Local Residual Learning allowing the less important information such as thin haze region or low-frequency to be bypassed through multiple local residual connections, let main network architecture focus on more effective information. 3) An Attention-based different levels Feature Fusion (FFA) structure, the feature weights are adaptively learned from the Feature Attention (FA) module, giving more weight to important features. This structure can also retain the information of shallow layers and pass it into deep layers.The experimental results demonstrate that our proposed FFA-Net surpasses previous state-of-the-art single image dehazing methods by a very large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 30.23 dB to 36.39 dB on the SOTS indoor test dataset. Code has been made available at GitHub.

AFF‐Dehazing: Attention‐based feature fusion network for low‐light image Dehazing

2021 ◽  
Author(s):  
Yu Zhou ◽  
Zhihua Chen ◽  
Bin Sheng ◽  
Ping Li ◽  
Jinman Kim ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Low Light ◽  
Image Dehazing ◽  
Light Image
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