Deep Neural Network Regularization (DNNR) on Denoised Image

Image dehazing in supervised learning models suffers from overfitting and underfitting problems. To avoid overfitting, the authors use regularization techniques like dropout and L2 norm. Dropout helps in reducing overfitting and batch normalization reduces the training time. In this paper, they have conducted experiments to analyze combination of various hyperparameters to have better network performance using deep neural network (DNN) on cifar10 dataset. The qualitative and quantitative study is performed by estimating the accuracy of the model on training and test images using with and without batch normalization. The proposed model performs better and is more stable. The results shows that dropout regularization technique is better than L2 technique containing hidden layers with large neurons. The paper assesses performance of DNN for any denoised model with the techniques like batch normalization and dropout, feature map, and adding more layers to the network. The authors quantitatively identify the value model loss and accuracy with the absence and presence of these parameters.