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Generally, the performance of deep learning-based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L-PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state-of-the-art methods.

Currently, deep learning methods have been widely applied to many fields like classification. Generally, these methods use the technology like transferring to make a model work well on different do...

Currently, image denoising is a challenge in many applications of computer vision. The existing denoising methods depend on the information of noise types or levels, which are generally classified by experts. These methods have not applied computational methods to pre-classify the image noise types. Furthermore, some methods assume that the noise type of the image is a certain one like Gaussian noise, which limits the ability of the denoising in real applications. Different from the existing methods, this paper introduces a new method that can classify and denoise not only a certain type noise but also mixed types of noises for real demand. Our method utilizes two types of deep learning networks. One is used to classify the noise type of the images and the other one performs denoising based on the classification result of the first one. Our framework can automatically denoise single or mixed types of noises with these efforts. Our experimental results show that our classification network achieves higher accuracy, and our denoising network can ensure higher PSNR and SSIM values than the existing methods.

Recently, deep learning has developed rapidly and contributed in many fields like the classification in radar and sonar applications. In some special fields like the underwater acoustic signals, th...

In real-world applications, different problems can adopt different models. Most of the existing denoising methods use the framework of deep learning, and the most commonly used denoised algorithm evaluation indicators, such as PSNR, MSE, etc., all without exception, require pictures’ ground truth which is needed as a reference. However, there are few real and noise-free pictures in the field of image denoising, only the noise reduction map can be compared with the noise map, which seems to be less persuasive. Therefore, this paper proposes a new criterion for judging the denoising model. The most important thing is that this method does not require noiseless images compared to PSNR when testing. Moreover, we improved the denoising model and verified the reliability of the criterion. At the same time, we conduct statistics on the recognition rate of different types of targets, and analyze the trend of misjudgment. In this paper, the synthetic aperture radar (SAR) image dataset is used as an experimental sample, and different noise parameters are used to obtain denoising data sets with different noise levels. Then we use different denoising models such as DN-CNN to process the data set. Finally, the CNN classification model is used for screening comparison. In this paper, the experimental results show that it is feasible to use classification to judge denoising, so based on this feasibility, this paper modified the denoising network and used classification to judge. The results show that the denoising effect is better and the classification accuracy is higher, that is, the denoising and classification are a complementary relationship.

Recently, deep learning has developed rapidly, which has made significant progress in tasks such as target detection and classification. Compared with traditional methods, using deep learning techniques contribute to achieve higher detection accuracy, recognition rate, and other better performance with big data set. In the fields of radar and sonar especially like underwater acoustic signals, training samples are scarce due to the difficulty of the collection or security reason, which leads to poor performance of the classification models, as those need big training samples. In this paper, we present a novel framework based on Generative Adversarial Networks (GAN) to resolve the problem of insufficient samples for the underwater acoustic signals. Our method preprocesses the audio samples to the gray-scale spectrum images, so that, those can fit the GAN to captures the features and reduce the complexity at the same time. Then our method utilizes an independent classification network outside the GAN to evaluate the generated samples by GAN. The experimental results show that the samples generated by our approach outperform existing methods with higher quality, which can significantly improve the prediction accuracy of the classification model.

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.