High-frequency details enhancing DenseNet for super-resolution

Zhou, Fuqiang; Li, Xiaojie; Li, Zuoxin

doi:10.1016/j.neucom.2018.02.027

Cited by 27 publications

(10 citation statements)

References 13 publications

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“…Traditional neural networks to improve image quality are convolutional neural network (CNN) and generative adversarial network (GAN). One resolution improvement technique known as super-resolution CNN (SRCNN) estimates unsharpness through the trained neural network and restores the resolution of the blurred input image 8,[10][11][12][13][14][15][16][17][18] . As CPU and GPU technologies develop, very deep convolution layers of neural networks can be calculated.…”

Section: Deep Learning For High-resolution and High-sensitivity Intermentioning

confidence: 99%

Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging

Lee

Kim

et al. 2020

Sci Rep

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Section: Deep Learning For High-resolution and High-sensitivity Intermentioning

confidence: 99%

Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging

Lee

Kim

et al. 2020

Sci Rep

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“…This means that enhancing bird shape and shallow image features is helpful to improve the classification performance of CNN models. Research results on attention mechanism techniques confirm that reinforcing image features in the target region brings a positive effect on model performance [ 24 , 27 , 28 ], while the success of deep dense networks also shows the positive effect of reusing shallow network features [ 29 , 30 , 31 , 32 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

Eyebirds: Enabling the Public to Recognize Water Birds at Hand

Jiaogen

Wang

Zhang

et al. 2022

Animals

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Enabling the public to easily recognize water birds has a positive effect on wetland bird conservation. However, classifying water birds requires advanced ornithological knowledge, which makes it very difficult for the public to recognize water bird species in daily life. To break the knowledge barrier of water bird recognition for the public, we construct a water bird recognition system (Eyebirds) by using deep learning, which is implemented as a smartphone app. Eyebirds consists of three main modules: (1) a water bird image dataset; (2) an attention mechanism-based deep convolution neural network for water bird recognition (AM-CNN); (3) an app for smartphone users. The waterbird image dataset currently covers 48 families, 203 genera and 548 species of water birds worldwide, which is used to train our water bird recognition model. The AM-CNN model employs attention mechanism to enhance the shallow features of bird images for boosting image classification performance. Experimental results on the North American bird dataset (CUB200-2011) show that the AM-CNN model achieves an average classification accuracy of 85%. On our self-built water bird image dataset, the AM-CNN model also works well with classification accuracies of 94.0%, 93.6% and 86.4% at three levels: family, genus and species, respectively. The user-side app is a WeChat applet deployed in smartphones. With the app, users can easily recognize water birds in expeditions, camping, sightseeing, or even daily life. In summary, our system can bring not only fun, but also water bird knowledge to the public, thus inspiring their interests and further promoting their participation in bird ecological conservation.

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“…While increasing the depth of the convolutional neural network had its benefits, there existed the challenge of vanishing information about the gradient or the input when going through the layers [29]. The authors proposed a simple connectivity-based architecture to facilitate the maximum flow of information through the layers through both the forward computation and the backward computation.…”

Section: Densenetmentioning

confidence: 99%

Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

Aquil

Ishak

2021

IJ-AI

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<span id="docs-internal-guid-01580d49-7fff-6f2a-70d1-7893ec0a6e14"><span>Plant diseases are a major cause of destruction and death of most plants and especially trees. However, with the help of early detection, this issue can be solved and treated appropriately. A timely and accurate diagnosis is critical in maintaining the quality of crops. Recent innovations in the field of deep learning (DL), especially in convolutional neural networks (CNNs) have achieved great breakthroughs across different applications such as the classification of plant diseases. This study aims to evaluate scratch and pre-trained CNNs in the classification of tomato plant diseases by comparing some of the state-of-the-art architectures including densely connected convolutional network (Densenet) 120, residual network (ResNet) 101, ResNet 50, ReseNet 30, ResNet 18, squeezenet and Vgg.net. The comparison was then evaluated using a multiclass statistical analysis based on the F-Score, specificity, sensitivity, precision, and accuracy. The dataset used for the experiments was drawn from 9 classes of tomato diseases and a healthy class from PlantVillage. The findings show that the pretrained Densenet-120 performed excellently with 99.68% precision, 99.84% F-1 score, and 99.81% accuracy, which is higher compared to its non-trained based model showing the effectiveness of using a combination of a CNN model with fine-tuning adjustment in classifying crop diseases.</span></span>

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High-frequency details enhancing DenseNet for super-resolution

Cited by 27 publications

References 13 publications

Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging

Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging

Eyebirds: Enabling the Public to Recognize Water Birds at Hand

Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

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