A novel image super-resolution reconstruction algorithm based on improved GANs and gradient penalty

Purpose–Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing.In order to solve such problems,the purposeof this paperis to proposea novel image super-resolutionalgorithmbasedon improved generative adversarial networks(GANs)with Wasserstein distance and gradient penalty.Design/methodology/approach–The proposed algorithm first introduces the conventional GANs architecture,the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction(SRWGANs-GP).In addition,a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution reconstruction.The content loss is extracted from the deep model’s feature maps,and such features are introduced to calculate mean square error(MSE)for the loss calculation of generators.Findings–To validate the effectiveness and feasibility of the proposed algorithm,a lot of compared experiments are applied on three common data sets,i.e.Set5,Set14 and BSD100.Experimental results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively convergence.Compared with the baseline deep models,the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution reconstruction.The MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and texture.Originality/value–Compared with the state-of-the-art algorithms,the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.

Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

Plant diseases have become a challenging threat in the agricultural field.Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early.However,deep learning entails extensive data for training,and it may be challenging to collect plant datasets.Even though plant datasets can be collected,they may be uneven in quantity.As a result,the problem of classification model overfitting arises.This study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available data.First,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image quality.Then,a gradient penalty mechanism was added to the loss function to improve the training stability of the model.Experiments show that the proposed method can best improve the recognition accuracy of the classifier with the doubled dataset.On AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the experiment.Moreover,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original dataset.Furthermore,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this approach.The general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.

An improved bidirectional generative adversarial network model for multivariate estimation of correlated and imbalanced tunnel construction parameters

Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.