An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution

Wang, Wei; Jiang, Yongbin; Luo, Yanhong; Ji, Li; Wang, Xin; Zhang, Tong

doi:10.2991/ijcis.d.191209.001

Cited by 60 publications

(24 citation statements)

References 18 publications

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“…(1) Difference from DRDN [33]: In DRDN, there are dense block (DB) structures for feature exploitation. e entire DRDN holds a global residual dense connection design to efficiently process the features.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Cao

Liu

Duan

et al. 2020

Scientific Programming

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Single image super-resolution (SISR) is a traditional image restoration problem. Given an image with low resolution (LR), the task of SISR is to find the homologous high-resolution (HR) image. As an ill-posed problem, there are works for SISR problem from different points of view. Recently, deep learning has shown its amazing performance in different image processing tasks. There are works for image super-resolution based on convolutional neural network (CNN). In this paper, we propose an adaptive residual channel attention network for image super-resolution. We first analyze the limitation of residual connection structure and propose an adaptive design for suitable feature fusion. Besides the adaptive connection, channel attention is proposed to adjust the importance distribution among different channels. A novel adaptive residual channel attention block (ARCB) is proposed in this paper with channel attention and adaptive connection. Then, a simple but effective upscale block design is proposed for different scales. We build our adaptive residual channel attention network (ARCN) with proposed ARCBs and upscale block. Experimental results show that our network could not only achieve better PSNR/SSIM performances on several testing benchmarks but also recover structural textures more effectively.

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Section: Discussionmentioning

confidence: 99%

Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Cao

Liu

Duan

et al. 2020

Scientific Programming

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“…Kaiming He et al proposed a residual net (RESNET) [24], which solved the problem of network degradation by using skip connections. Wang et al proposed a deep residual dense network based on ResNet for recognizing super-resolution images with high accuracy [25].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Using CFW-Net Deep Learning Models for X-Ray Images to Detect COVID-19 Patients

Wang¹,

Líu²,

Li³

et al. 2020

IJCIS

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COVID-19 is an infectious disease caused by severe acute respiratory syndrome (SARS)-CoV-2 virus. So far, more than 20 million people have been infected. With the rapid spread of COVID-19 in the world, most countries are facing the shortage of medical resources. As the most extensive detection technology at present, reverse transcription polymerase chain reaction (RT-PCR) is expensive, long-time (time consuming) and low sensitivity. These problems prompted us to propose a deep learning model to help radiologists and clinicians detect COVID-19 cases through chest X-ray. According to the characteristics of chest X-ray image, we designed the channel feature weight extraction (CFWE) module, and proposed a new convolutional neural network, CFW-Net, based on the CFWE module. Meanwhile, in order to improve recognition efficiency, the network adopts three classifiers for classification: one fully connected (FC) layers, global average pooling fully-connected (GFC) module and point convolution global average pooling (CGAP) module. The latter two methods have fewer parameters, less calculation and better real-time performance. In this paper, we have evaluated CFW-Net based on two open-source datasets. The experimental results show that the overall accuracy of our model CFW-Net56-GFC is 94.35% and the accuracy and sensitivity of COVID-19 are 100%. Compared with other methods, our method can detect COVID-19 disease more accurately.

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“…7. ResNets are stacked by residual blocks, so the network is easier to optimize and its depth can be greatly increased, both of which can improve the recognition accuracy [31].…”

Section: Resnetsmentioning

confidence: 99%

An improved deep learning approach and its applications on colonic polyp images detection

et al. 2020

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Background: Colonic polyps are more likely to be cancerous, especially those with large diameter, large number and atypical hyperplasia. If colonic polyps cannot be treated in early stage, they are likely to develop into colon cancer. Colonoscopy is easily limited by the operator's experience, and factors such as inexperience and visual fatigue will directly affect the accuracy of diagnosis. Cooperating with Hunan children's hospital, we proposed and improved a deep learning approach with global average pooling (GAP) in colonoscopy for assisted diagnosis. Our approach for assisted diagnosis in colonoscopy can prompt endoscopists to pay attention to polyps that may be ignored in real time, improve the detection rate, reduce missed diagnosis, and improve the efficiency of medical diagnosis. Methods: We selected colonoscopy images from the gastrointestinal endoscopy room of Hunan children's hospital to form the colonic polyp datasets. And we applied the image classification method based on Deep Learning to the classification of Colonic Polyps. The classic networks we used are VGGNets and ResNets. By using global average pooling, we proposed the improved approaches: VGGNets-GAP and ResNets-GAP. Results: The accuracies of all models in datasets exceed 98%. The TPR and TNR are above 96 and 98% respectively. In addition, VGGNets-GAP networks not only have high classification accuracies, but also have much fewer parameters than those of VGGNets. Conclusions: The experimental results show that the proposed approach has good effect on the automatic detection of colonic polyps. The innovations of our method are in two aspects: (1) the detection accuracy of colonic polyps has been improved. (2) our approach reduces the memory consumption and makes the model lightweight. Compared with the original VGG networks, the parameters of our VGG19-GAP networks are greatly reduced.

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An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution

Cited by 60 publications

References 18 publications

Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Using CFW-Net Deep Learning Models for X-Ray Images to Detect COVID-19 Patients

An improved deep learning approach and its applications on colonic polyp images detection

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