Super-resolution reconstruction based on compressed sensing and deep learning model

2016 ◽  
Author(s):  
Dan Sun ◽  
Tianyang Zhang ◽  
Lisha Chen
Keyword(s):  
Deep Learning ◽  
Compressed Sensing ◽  
Super Resolution ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Recognition of Vehicle License Plates Based on Image Processing

2021 ◽  
Vol 11 (14) ◽  
pp. 6292
Author(s):  
Tae-Gu Kim ◽  
Byoung-Ju Yun ◽  
Tae-Hun Kim ◽  
Jae-Young Lee ◽  
Kil-Houm Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Recognition Rate ◽  
Super Resolution ◽  
Learning Model ◽  
Distortion Correction ◽  
License Plate ◽  
Generative Adversarial Network ◽  
Perspective Distortion ◽  
Deep Learning Model

In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images.

scholarly journals Deep learning can accelerate and quantify simulated localized correlated spectroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zohaib Iqbal ◽  
Dan Nguyen ◽  
Michael Albert Thomas ◽  
Steve Jiang
Keyword(s):  
Deep Learning ◽  
Compressed Sensing ◽  
Mean Squared Error ◽  
Learning Model ◽  
The Body ◽  
Resonance Spectroscopy ◽  
Atomic Structures ◽  
Squared Error ◽  
Deep Learning Model ◽  
Cosy Spectra

AbstractNuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.

scholarly journals Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

2021 ◽  
pp. 102196
Author(s):  
Chompunuch Sarasaen ◽  
Soumick Chatterjee ◽  
Mario Breitkopf ◽  
Georg Rose ◽  
Andreas Nürnberger ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prior Knowledge ◽  
Super Resolution ◽  
Dynamic Mri ◽  
Learning Model ◽  
Fine Tuning ◽  
Model Parameters ◽  
Deep Learning Model

scholarly journals Model Specialization for the Use of ESRGAN on Satellite and Airborne Imagery

2021 ◽  
Vol 13 (20) ◽  
pp. 4044
Author(s):  
Étienne Clabaut ◽  
Myriam Lemelin ◽  
Mickaël Germain ◽  
Yacine Bouroubi ◽  
Tony St-Pierre
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Texture Analysis ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Learning Model ◽  
Generative Adversarial Networks ◽  
Remote Sensing Images ◽  
Adversarial Networks ◽  
Deep Learning Model

Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interesting to specialize the model with respect to a particular theme. The use of enhanced super-resolution generative adversarial networks (ERSGAN), a specific type of deep learning architecture, allows the spatial resolution of remote sensing images to be increased by “hallucinating” non-existent details. In this study, we show that ESRGAN create better quality images when trained on thematically classified images than when trained on a wide variety of examples. All things being equal, we further show that the algorithm performs better on some themes than it does on others. Texture analysis shows that these performances are correlated with the inverse difference moment and entropy of the images.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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