RDA- CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification

Sathya, K.; Rajalakshmi, M.

doi:10.32604/csse.2022.022206

Cited by 18 publications

(6 citation statements)

References 30 publications

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“…Over the years Super-Resolution achievements in the non-computer vision domain have proliferated [18,19], and unlike image processing [20][21][22], it is critical to understand and quantify forecast uncertainty in climate and climate applications. The classical precipitation downscaling algorithm [23] uses techniques such as stochastic autoregressive models.…”

Section: Related Workmentioning

confidence: 99%

Using GAN Neural Networks for Super-Resolution Reconstruction of Temperature Fields

Li¹,

Jiang²,

Han³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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A Generative Adversarial Neural (GAN) network is designed based on deep learning for the Super-Resolution (SR) reconstruction task of temperature fields (comparable to downscaling in the meteorological field), which is limited by the small number of ground stations and the sparse distribution of observations, resulting in a lack of fineness of data. To improve the network's generalization performance, the residual structure, and batch normalization are used. Applying the nearest interpolation method to avoid over-smoothing of the climate element values instead of the conventional Bicubic interpolation in the computer vision field. Sub-pixel convolution is used instead of transposed convolution or interpolation methods for up-sampling to speed up network inference. The experimental dataset is the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) with a bidirectional resolution of 0:1 Â 0:1 . On the other hand, the task aims to scale up the size by a factor of 8, which is rare compared to conventional methods. The comparison methods include traditional interpolation methods and a more widely used GAN-based network such as the SRGAN. The final experimental results show that the proposed scheme advances the performance of Root Mean Square Error (RMSE) by 37.25%, the Peak Signal-tonoise Ratio (PNSR) by 14.4%, and the Structural Similarity (SSIM) by 10.3% compared to the Bicubic Interpolation. For the traditional SRGAN network, a relatively obvious performance improvement is observed by experimental demonstration. Meanwhile, the GAN network can converge stably and reach the approximate Nash equilibrium for various initialization parameters to empirically illustrate the effectiveness of the method in the temperature fields.

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Section: Related Workmentioning

confidence: 99%

Using GAN Neural Networks for Super-Resolution Reconstruction of Temperature Fields

Li¹,

Jiang²,

Han³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…Convolutional Neural Networks (CNN) is a feedforward neural network with a structure to convolution calculation [25]. It has been successfully applied in many practical fields, including image classification, speech recognition, and natural language processing [26][27][28][29][30][31][32][33][34][35].…”

Section: Cnnmentioning

confidence: 99%

Compiler IR-Based Program Encoding Method for Software Defect Prediction

Chen¹,

Chen²,

He³

et al. 2022

Computers, Materials &Amp; Continua

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With the continuous expansion of software applications, people's requirements for software quality are increasing. Software defect prediction is an important technology to improve software quality. It often encodes the software into several features and applies the machine learning method to build defect prediction classifiers, which can estimate the software areas is clean or buggy. However, the current encoding methods are mainly based on the traditional manual features or the AST of source code. Traditional manual features are difficult to reflect the deep semantics of programs, and there is a lot of noise information in AST, which affects the expression of semantic features. To overcome the above deficiencies, we combined with the Convolutional Neural Networks (CNN) and proposed a novel compiler Intermediate Representation (IR) based program encoding method for software defect prediction (CIR-CNN). Specifically, our program encoding method is based on the compiler IR, which can eliminate a large amount of noise information in the syntax structure of the source code and facilitate the acquisition of more accurate semantic information. Secondly, with the help of data flow analysis, a Data Dependency Graph (DDG) is constructed on the compiler IR, which helps to capture the deeper semantic information of the program. Finally, we use the widely used CNN model to build a software defect prediction model, which can increase the adaptive ability of the method. To evaluate the performance of the CIR-CNN, we use seven projects from PROMISE datasets to set up comparative experiments. The experiments results show that, in WPDP, with our CIR-CNN method, the prediction accuracy was improved by 12% for the AST-encoded CNN-based model and by 20.9% for the traditional features-based LR model, respectively. And in CPDP, the AST-encoded DBNbased model was improved by 9.1% and the traditional features-based TCA+ model by 19.2%, respectively.

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“…So, it is desired to produce images of higher resolution than the imaging device. Over the years, super resolution (SR) algorithms have gained a lot of attention due to their widespread applications [1][2][3] in remote sensing, object detection [4], surveillance monitoring, etc. Single image super resolution (SISR) techniques aim to recover images of higher resolution from the corresponding LR images.…”

Section: Introductionmentioning

confidence: 99%

Deep Learned Singular Residual Network for Super Resolution Reconstruction

Suryanarayana¹,

Bhavana²,

Prasad³

et al. 2023

Computers, Materials &Amp; Continua

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Single image super resolution (SISR) techniques produce images of high resolution (HR) as output from input images of low resolution (LR). Motivated by the effectiveness of deep learning methods, we provide a framework based on deep learning to achieve super resolution (SR) by utilizing deep singular-residual neural network (DSRNN) in training phase. Residuals are obtained from the difference between HR and LR images to generate LR-residual example pairs. Singular value decomposition (SVD) is applied to each LR-residual image pair to decompose into subbands of low and high frequency components. Later, DSRNN is trained on these subbands through input and output channels by optimizing the weights and biases of the network. With fewer layers in DSRNN, the influence of exploding gradients is reduced. This speeds up the learning process and also improves accuracy by using skip connections. The trained DSRNN parameters yield residuals to recover the HR subbands in the testing phase. Experimental analysis shows that the proposed method results in superior performance to existing methods in terms of subjective quality. Extensive testing results on popular benchmark datasets such as set5, set14, and urban100 for a scaling factor of 4 show the effectiveness of the proposed method across different qualitative evaluation metrics.

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RDA- CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification

Cited by 18 publications

References 30 publications

Using GAN Neural Networks for Super-Resolution Reconstruction of Temperature Fields

Using GAN Neural Networks for Super-Resolution Reconstruction of Temperature Fields

Compiler IR-Based Program Encoding Method for Software Defect Prediction

Deep Learned Singular Residual Network for Super Resolution Reconstruction

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