scholarly journals Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

2020 ◽  
Vol 10 (3) ◽  
pp. 1190
Author(s):  
Samia Haouassi ◽  
Di Wu
Keyword(s):  
State Of The Art ◽  
Estimation Algorithm ◽  
Surveillance Systems ◽  
Security Systems ◽  
High Quality ◽  
Image Dehazing ◽  
Multi Scale ◽  
Second Stage ◽  
Computer Vision Applications ◽  
Atmospheric Light

Image dehazing plays a pivotal role in numerous computer vision applications such as object recognition, surveillance systems, and security systems, where it can be considered as an introductory stage. Recently, many proposed learning-based works address this significant task; however, most of them neglect the atmospheric light estimation and fail to produce accurate transmission maps. To address such a problem, in this paper, we propose a two-stage dehazing system. The first stage presents an accurate atmospheric light algorithm labeled “A-Est” that employs hazy image blurriness and quadtree decomposition. Te second stage represents a cascaded multi-scale CNN model called CMT n e t that consists of two subnetworks, one for calculating rough transmission maps (CMCNN t r ) and the other for its refinement (CMCNN t ). Each subnetwork is composed of three-layer D-units (D indicates dense). Experimental analysis and comparisons with state-of-the-art dehazing methods revealed that the proposed system can estimate AL and t efficiently and accurately by achieving high-quality dehazing results and outperforms state-of-the-art comparative methods according to SSIM and MSE values, where our proposed achieves the best scores of both (91% average SSIM and 0.068 average MSE).

scholarly journals Multiscale Single Image Dehazing Based on Adaptive Wavelet Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Wang ◽  
Wenhui Li ◽  
Qingji Guan ◽  
Miao Qi
Keyword(s):  
State Of The Art ◽  
Local Similarity ◽  
Fusion Method ◽  
Single Image ◽  
Image Dehazing ◽  
Adaptive Wavelet ◽  
Atmospheric Scattering ◽  
Dark Channel ◽  
Computer Vision Applications ◽  
Scene Depth

Removing the haze effects on images or videos is a challenging and meaningful task for image processing and computer vision applications. In this paper, we propose a multiscale fusion method to remove the haze from a single image. Based on the existing dark channel prior and optics theory, two atmospheric veils with different scales are first derived from the hazy image. Then, a novel and adaptive local similarity-based wavelet fusion method is proposed for preserving the significant scene depth property and avoiding blocky artifacts. Finally, the clear haze-free image is restored by solving the atmospheric scattering model. Experimental results demonstrate that the proposed method can yield comparative or even better results than several state-of-the-art methods by subjective and objective evaluations.

scholarly journals Dual-Path in Dual-Path Network for Single Image Dehazing

2019 ◽  
Author(s):  
Aiping Yang ◽  
Haixin Wang ◽  
Zhong Ji ◽  
Yanwei Pang ◽  
Ling Shao
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Single Image ◽  
Image Dehazing ◽  
Popular Approach ◽  
Haze Removal ◽  
Image Blurring ◽  
Atmospheric Light ◽  
Single Image Dehazing ◽  
Structural Layer

Recently, deep learning-based single image dehazing method has been a popular approach to tackle dehazing. However, the existing dehazing approaches are performed directly on the original hazy image, which easily results in image blurring and noise amplifying. To address this issue, the paper proposes a DPDP-Net (Dual-Path in Dual-Path network) framework by employing a hierarchical dual path network. Specifically, the first-level dual-path network consists of a Dehazing Network and a Denoising Network, where the Dehazing Network is responsible for haze removal in the structural layer, and the Denoising Network deals with noise in the textural layer, respectively. And the second-level dual-path network lies in the Dehazing Network, which has an AL-Net (Atmospheric Light Network) and a TM-Net (Transmission Map Network), respectively. Concretely, the AL-Net aims to train the non-uniform atmospheric light, while the TM-Net aims to train the transmission map that reflects the visibility of the image. The final dehazing image is obtained by nonlinearly fusing the output of the Denoising Network and the Dehazing Network. Extensive experiments demonstrate that our proposed DPDP-Net achieves competitive performance against the state-of-the-art methods on both synthetic and real-world images.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals Global optimization of an encapsulated Si/SiO$$_2$$ L3 cavity with a 43 million quality factor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. P. Vasco ◽  
V. Savona
Keyword(s):  
Global Optimization ◽  
Photonic Crystal ◽  
Quality Factor ◽  
State Of The Art ◽  
Optimization Strategy ◽  
High Quality ◽  
Optimal Value ◽  
Out Of Plane ◽  
Fabrication Tolerances ◽  
Structural Imperfections

AbstractWe optimize a silica-encapsulated silicon L3 photonic crystal cavity for ultra-high quality factor by means of a global optimization strategy, where the closest holes surrounding the cavity are varied to minimize out-of-plane losses. We find an optimal value of $$Q_c=4.33\times 10^7$$ Q c = 4.33 × 10 7 , which is predicted to be in the 2 million regime in presence of structural imperfections compatible with state-of-the-art silicon fabrication tolerances.

scholarly journals An All-Batch Loss for Constructing Prediction Intervals

2021 ◽  
Vol 11 (4) ◽  
pp. 1728
Author(s):  
Hua Zhong ◽  
Li Xu
Keyword(s):  
Gradient Descent ◽  
State Of The Art ◽  
Prediction Interval ◽  
Feedforward Neural Networks ◽  
Important Research ◽  
Likelihood Principle ◽  
High Quality ◽  
Construction Methods ◽  
Important Research Topic ◽  
Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

scholarly journals PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

2021 ◽  
Vol 13 (7) ◽  
pp. 1243
Author(s):  
Wenxin Yin ◽  
Wenhui Diao ◽  
Peijin Wang ◽  
Xin Gao ◽  
Ya Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Power Plants ◽  
State Of The Art ◽  
Thermal Power ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Thermal Power Plants ◽  
Average Precision ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Majid Yekkehfallah ◽  
Ming Yang ◽  
Zhiao Cai ◽  
Liang Li ◽  
Chuanxiang Wang
Keyword(s):  
Mobile Robots ◽  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
State Of The Art ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
3D Localization ◽  
Fast Motion ◽  
Tof Camera

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

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