scholarly journals Deep Residual Network with Sparse Feedback for Image Restoration

2018 ◽  
Vol 8 (12) ◽  
pp. 2417 ◽  
Author(s):  
Zhenyu Guo ◽  
Yujuan Sun ◽  
Muwei Jian ◽  
Xiaofeng Zhang
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Deep Neural Network ◽  
Convergence Speed ◽  
Fast Convergence ◽  
Experimental Results ◽  
Residual Network ◽  
Unknown Parameters ◽  
Noisy Images ◽  
Blurred Images

A deep neural network is difficult to train due to a large number of unknown parameters. To increase trainable performance, we present a moderate depth residual network for the restoration of motion blurring and noisy images. The proposed network has only 10 layers, and the sparse feedbacks are added in the middle and the last layers, which are called FbResNet. FbResNet has fast convergence speed and effective denoising performance. In addition, it can also reduce the artificial Mosaic trace at the seam of patches, and visually pleasant output results can be produced from the blurred images or noisy images. Experimental results show the effectiveness of our designed model and method.

A UMDA-Based Discretization Method for Continuous Attributes

2011 ◽  
Vol 403-408 ◽  
pp. 1834-1838
Author(s):  
Jing Zhao ◽  
Chong Zhao Han ◽  
Bin Wei ◽  
De Qiang Han
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Evolutionary Algorithms ◽  
Marginal Distribution ◽  
Convergence Speed ◽  
Fast Convergence ◽  
Experimental Results ◽  
Discretization Method ◽  
Bottom Up ◽  
Global Dynamic

Discretization of continuous attributes have played an important role in machine learning and data mining. They can not only improve the performance of the classifier, but also reduce the space of the storage. Univariate Marginal Distribution Algorithm is a modified Evolutionary Algorithms, which has some advantages over classical Evolutionary Algorithms such as the fast convergence speed and few parameters need to be tuned. In this paper, we proposed a bottom-up, global, dynamic, and supervised discretization method on the basis of Univariate Marginal Distribution Algorithm.The experimental results showed that the proposed method could effectively improve the accuracy of classifier.

scholarly journals Denoising Prior Driven Deep Neural Network for Image Restoration

2019 ◽  
Vol 41 (10) ◽  
pp. 2305-2318 ◽  
Author(s):  
Weisheng Dong ◽  
Peiyao Wang ◽  
Wotao Yin ◽  
Guangming Shi ◽  
Fangfang Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Deep Neural Network

scholarly journals Environmental Attention-Guided Branchy Neural Network for Speech Enhancement

2020 ◽  
Vol 10 (3) ◽  
pp. 1167 ◽  
Author(s):  
Lu Zhang ◽  
Mingjiang Wang ◽  
Qiquan Zhang ◽  
Ming Liu
Keyword(s):  
Neural Network ◽  
Noise Reduction ◽  
Speech Enhancement ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Noisy Environments ◽  
Neural Structure ◽  
Noise Interference ◽  
Noise Type ◽  
Speech Reconstruction

The performance of speech enhancement algorithms can be further improved by considering the application scenarios of speech products. In this paper, we propose an attention-based branchy neural network framework by incorporating the prior environmental information for noise reduction. In the whole denoising framework, first, an environment classification network is trained to distinguish the noise type of each noisy speech frame. Guided by this classification network, the denoising network gradually learns respective noise reduction abilities in different branches. Unlike most deep neural network (DNN)-based methods, which learn speech reconstruction capabilities with a common neural structure from all training noises, the proposed branchy model obtains greater performance benefits from the specially trained branches of prior known noise interference types. Experimental results show that the proposed branchy DNN model not only preserved better enhanced speech quality and intelligibility in seen noisy environments, but also obtained good generalization in unseen noisy environments.

Deep Learning for Detecting Sleep Apnea from ECG Signals

2020 ◽  
Vol 10 (6) ◽  
pp. 1265-1273
Author(s):  
Lili Chen ◽  
Huoyao Xu
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Ecg Signals ◽  
Novel Approach ◽  
Learning Machine ◽  
High Level ◽  
Electrocardiogram Ecg

Sleep apnea (SA) is a common sleep disorders affecting the sleep quality. Therefore the automatic SA detection has far-reaching implications for patients and physicians. In this paper, a novel approach is developed based on deep neural network (DNN) for automatic diagnosis SA. To this end, five features are extracted from electrocardiogram (ECG) signals through wavelet decomposition and sample entropy. The deep neural network is constructed by two-layer stacked sparse autoencoder (SSAE) network and one softmax layer. The softmax layer is added at the top of the SSAE network for diagnosing SA. Afterwards, the SSAE network can get more effective high-level features from raw features. The experimental results reveal that the performance of deep neural network can accomplish an accuracy of 96.66%, a sensitivity of 96.25%, and a specificity of 97%. In addition, the performance of deep neural network outperforms the comparison models including support vector machine (SVM), random forest (RF), and extreme learning machine (ELM). Finally, the experimental results reveal that the proposed method can be valid applied to automatic SA event detection.

scholarly journals Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199218
Author(s):  
Wei Zhang ◽  
Xurong Liu ◽  
Zhiwen Huang ◽  
Jianmin Zhu
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Deep Neural Network ◽  
Search Algorithm ◽  
Parameters Identification ◽  
Experimental Results ◽  
Neural Network Modeling ◽  
Dynamic Parameters ◽  
Stiffness And Damping ◽  
Sliding Joints

Dynamic parameters of joints are indispensable factors affecting performance of machine tools. In order to obtain the stiffness and damping of sliding joints between the working platform and the machine tool body of the surface grinder, a new method of dynamic parameters identification is proposed that based on deep neural network (DNN) modeling. Firstly, the DNN model of dynamic parameters for working platform-machine tool body sliding joints is established by taking the stiffness and damping parameters as the input and the natural frequencies as the output. Secondly, the number of hidden layers in DNN topology is optimally selected in order to the optimal training results. Thirdly, combining the predicted results by DNN model with experimental results by modal test, the stiffness and damping are identified via cuckoo search algorithm. Finally, the relative error between the predicted and experimental results is less than 2.2%, which achieves extremely high prediction precision; and thereby indicates the feasibility and effectiveness of the proposed method.

scholarly journals Speech enhancement methods based on binaural cue coding

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xianyun Wang ◽  
Changchun Bao
Keyword(s):  
Neural Network ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Channel Correlation ◽  
Noisy Speech ◽  
Time Frequency ◽  
Improve Accuracy ◽  
Better Than

AbstractAccording to the encoding and decoding mechanism of binaural cue coding (BCC), in this paper, the speech and noise are considered as left channel signal and right channel signal of the BCC framework, respectively. Subsequently, the speech signal is estimated from noisy speech when the inter-channel level difference (ICLD) and inter-channel correlation (ICC) between speech and noise are given. In this paper, exact inter-channel cues and the pre-enhanced inter-channel cues are used for speech restoration. The exact inter-channel cues are extracted from clean speech and noise, and the pre-enhanced inter-channel cues are extracted from the pre-enhanced speech and estimated noise. After that, they are combined one by one to form a codebook. Once the pre-enhanced cues are extracted from noisy speech, the exact cues are estimated by a mapping between the pre-enhanced cues and a prior codebook. Next, the estimated exact cues are used to obtain a time-frequency (T-F) mask for enhancing noisy speech based on the decoding of BCC. In addition, in order to further improve accuracy of the T-F mask based on the inter-channel cues, the deep neural network (DNN)-based method is proposed to learn the mapping relationship between input features of noisy speech and the T-F masks. Experimental results show that the codebook-driven method can achieve better performance than conventional methods, and the DNN-based method performs better than the codebook-driven method.

scholarly journals Deep ChaosNet for Action Recognition in Videos

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Huafeng Chen ◽  
Maosheng Zhang ◽  
Zhengming Gao ◽  
Yunhong Zhao
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Experimental Results ◽  
Low Level ◽  
Hidden Layer ◽  
High Level ◽  
Standard Action

Current methods of chaos-based action recognition in videos are limited to the artificial feature causing the low recognition accuracy. In this paper, we improve ChaosNet to the deep neural network and apply it to action recognition. First, we extend ChaosNet to deep ChaosNet for extracting action features. Then, we send the features to the low-level LSTM encoder and high-level LSTM encoder for obtaining low-level coding output and high-level coding results, respectively. The agent is a behavior recognizer for producing recognition results. The manager is a hidden layer, responsible for giving behavioral segmentation targets at the high level. Our experiments are executed on two standard action datasets: UCF101 and HMDB51. The experimental results show that the proposed algorithm outperforms the state of the art.

A Motion Blurred Image Restoration Method Based on Wiener Filtering

2011 ◽  
Vol 403-408 ◽  
pp. 1664-1667 ◽  
Author(s):  
Qian Qian Quan
Keyword(s):  
Image Restoration ◽  
Motion Blur ◽  
Experimental Results ◽  
Wiener Filtering ◽  
Degradation Model ◽  
Image Blurring ◽  
Blurred Image ◽  
Working Principle ◽  
Restoration Method ◽  
Blurred Images

To the deficiencies of traditional methods for avoiding motion image blurring, a motion blur image restoration method is studied based on Wiener filtering in this paper. The formation factors of motion-blurred images and the imaging process are analyzed, and the motion blur degradation model is established. It introduced the working principle of Wiener filtering, described the steps of blurred image restoration in details. The experiment testing and data analyzing are also conducted. Experimental results showed that the method can has good performance.

scholarly journals Collaborative and Attentive Learning for Personalized Image Aesthetic Assessment

2018 ◽  
Author(s):  
Guolong Wang ◽  
Junchi Yan ◽  
Zheng Qin
Keyword(s):  
Neural Network ◽  
Collaborative Filtering ◽  
Deep Neural Network ◽  
Regions Of Interest ◽  
Experimental Results ◽  
Visual Images ◽  
Learning Method ◽  
Aesthetic Quality ◽  
The Public ◽  
Public Dataset

The ever-increasing volume of visual images has stimulated the demand for organizing such data by aesthetic quality. Automatic and especially learning based aesthetic assessment methods have shown potential by recent works. Existing image aesthetic prediction is often user-agnostic which may ignore the fact that the rating to an image can be inherently individual. We fill this gap by formulating the personalized image aesthetic assessment problem with a novel learning method. Specifically, we collect user-image textual reviews in addition with visual images from the public dataset to organize a review-augmented benchmark. Using this enriched dataset, we devise a deep neural network with a user/image relation encoding input for collaborative filtering. Meanwhile an attentive mechanism is designed to capture the user-specific taste for image semantic tags and regions of interest by fusing the image and user's review. Extensive and promising experimental results on the review-augmented benchmark corroborate the efficacy of our approach.

scholarly journals Sharpening of hierarchical visual feature representations of blurred images

2017 ◽  
Author(s):  
Mohamed Abdelhack ◽  
Yukiyasu Kamitani
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Human Brain ◽  
Deep Neural Network ◽  
Prior Information ◽  
Visual Input ◽  
Top Down ◽  
Bottom Up ◽  
Reference Space ◽  
Blurred Images

AbstractThe robustness of the visual system lies in its ability to perceive degraded images. This is achieved through interacting bottom-up, recurrent, and top-down pathways that process the visual input in concordance with stored prior information. The interaction mechanism by which they integrate visual input and prior information is still enigmatic. We present a new approach using deep neural network (DNN) representation to reveal the effects of such integration on degraded visual inputs. We transformed measured human brain activity resulting from viewing blurred images to the hierarchical representation space derived from a feedforward DNN. Transformed representations were found to veer towards the original non-blurred image and away from the blurred stimulus image. This indicated deblurring or sharpening in the neural representation, and possibly in our perception. We anticipate these results will help unravel the interplay mechanism between bottom-up, recurrent, and top-down pathways, leading to more comprehensive models of vision.Significance statementOne powerful characteristic of the visual system is its ability to complement visual information for incomplete visual images. It operates by projecting information from higher visual and semantic areas of the brain into the lower and mid-level representations of the visual stimulus. We investigate the mechanism by which the human brain represents blurred visual stimuli. By decoding fMRI activity into a feedforward-only deep neural network reference space, we found that neural representations of blurred images are biased towards their corresponding deblurred images. This indicates a sharpening mechanism occurring in the visual cortex.

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