Clear and clean underwater images can provide valuable information, which are crucial for developing, exploring, and protecting the underwater resources. However, the raw underwater image seldom fulfills the requireme...
详细信息
ISBN:
(纸本)9781728143286
Clear and clean underwater images can provide valuable information, which are crucial for developing, exploring, and protecting the underwater resources. However, the raw underwater image seldom fulfills the requirements concerning the underwater research due to the serious image degradation. To enhance single underwater image, we propose an effective and new method combined on the dehazing and color correction algorithm. First, we obtain the dehazed image by a fusion dehazing method calculated on the difference between maximum green-blue dark channels and maximum red dark channel, and the top 0.1% bright pixels in green-blue dark channels. Second, we obtain the enhanced image via the color restoration method corresponding to the human visual system. Finally, we use an efficient and simple weight fusion strategy to incorporate the dehazed image and the enhanced image for yielding the final high quality underwater image. Subjective assessment demonstrates that our method can remove haze, correct color cast, improve image brightness, and preserve image naturalness. The enhanced results obtain the highest values in terms of entropy, average gradient, UCIQE, UIQM, and CCF, which is superior to several existing underwater enhancement methods. Moreover, our method can improve other degraded image quality, such as low-light image and haze image, and can benefit the application tests, such as key points matching and edge detection.
Dynamic threshold neural P systems (DTNP systems) are a distributedparallel computing model with an interesting mechanism involving the cooperative spiking of neurons in a local region. In this paper, this mechanism ...
详细信息
Dynamic threshold neural P systems (DTNP systems) are a distributedparallel computing model with an interesting mechanism involving the cooperative spiking of neurons in a local region. In this paper, this mechanism is combined with the nonsubsampled contourlet transform (NSCT) to develop a novel fusion method for multi-modality medical images. The complementary information of multi-modality images is extracted using an improved novel sum-modified Laplacian (INSML) feature, which is used in the fusion rules for the low-frequency NSCT coefficients. Moreover, the high-frequency NSCT coefficients are extracted using the WLE-INSML features, which are used to construct the fusion rules for these coefficients. The proposed fusion method is evaluated on an open dataset consisting of twelve pairs of multi-modality medical images. In addition, it is compared with nine previously reported fusion methods and four deep learning based fusion methods. The qualitative and quantitative experimental results demonstrate the advantage of the proposed fusion method in terms of the visual quality and fusion performance. (C) 2020 Elsevier B.V. All rights reserved.
Modern large-scale distributed computing systems, processing large volumes of data, require mature monitoring systems able to control and track in resources, networks, computing tasks, queues and other components. In ...
详细信息
Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequently occurred erroneous behaviors. The...
详细信息
Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequently occurred erroneous behaviors. Therefore, it is necessary to conduct a systematical testing for DNNs before they are deployed to real-world applications. Existing testing methods have provided fine-grained metrics based on neuron coverage and proposed various approaches to improve such metrics. However, it has been gradually realized that a higher neuron coverage does not necessarily represent better capabilities in identifying defects that lead to errors. Besides, coverage-guided methods cannot hunt errors due to faulty training procedure. So the robustness improvement of DNNs via retraining by these testing examples are unsatisfactory. To address this challenge, we introduce the concept of excitable neurons based on Shapley value and design a novel white-box testing framework for DNNs, namely DeepSensor. It is motivated by our observation that neurons with larger responsibility towards model loss changes due to small perturbations are more likely related to incorrect corner cases due to potential defects. By maximizing the number of excitable neurons concerning various wrong behaviors of models, DeepSensor can generate testing examples that effectively trigger more errors due to adversarial inputs, polluted data and incomplete training. Extensive experiments implemented on both image classification models and speaker recognition models have demonstrated the superiority of DeepSensor. Compared with the state-of-the-art testing approaches, DeepSensor can find more test errors due to adversarial inputs (∼ ×1.2), polluted data (∼ ×5) and incompletely-trained DNNs (∼ ×1.3). Additionally, it can help DNNs build larger l2-norm robustness bound (∼ ×3) via retraining according to CLEVER's certification. We further provide interpretable proofs for effectiveness of DeepSensor via excitable neuro
The use of Deep Learning methods have been identified as a key opportunity for enabling processing of extreme-scale scientific datasets. Feeding data into compute nodes equipped with several high-end GPUs at sufficien...
详细信息
ISBN:
(纸本)9781728116440
The use of Deep Learning methods have been identified as a key opportunity for enabling processing of extreme-scale scientific datasets. Feeding data into compute nodes equipped with several high-end GPUs at sufficiently high rate is a known challenge. Facilitating processing of these datasets thus requires the ability to store petabytes of data as well as to access the data with very high bandwidth. In this work, we look at two Deep Learning use cases for cytoarchitectonic brain mapping. These applications are very challenging for the underlying IO system. We present an in depth analysis of their IO requirements and performance. Both applications are limited by the IO performance, as the training processes often have to wait several seconds for new training data. Both applications read random patches from a collection of large HDF5 datasets or TIFF files, which result in many small non-consecutive accesses to the parallel file systems. By using a chunked data format or storing temporally copies of the required patches, the IO performance can be improved significantly. These leads to a decrease of the total runtime of up to 80%.
In order to promote the visual quality of degraded underwater images, we develop an innovative variational model based on Retinex with a total generalized variation (TGV) prior on the illumination. The TGV prior is ad...
详细信息
ISBN:
(纸本)9781728143286
In order to promote the visual quality of degraded underwater images, we develop an innovative variational model based on Retinex with a total generalized variation (TGV) prior on the illumination. The TGV prior is adopted to approximate piecewise smoothness and piecewise linear smoothness of the illumination, which combines the first-order and second-order total variation (TV) to model the variation of illumination. When adopting this illumination prior in the Retinex-based algorithm, the illumination and reflection are well separated, and underwater enhanced results appear more natural and their details and edges are better preserved. Then an efficient iterative optimization method is derived to settle the proposed model via alternately calculating the illumination and the reflection simultaneously. Numerous experiments on both visual results and objective metrics demonstrate the superiority of our method compared with several underwater enhancement methods. In addition, the proposed method can be extended for dehazing, sandstorm removal and low illumination image enhancement, which can illustrate better capacity of our model.
Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is...
详细信息
Central Receiver Systems use thousands of heliostats distributed in a field to reflect the solar radiation to a receiver installed at a tower. The large distances between the heliostats and their aim point require a h...
详细信息
Central Receiver Systems use thousands of heliostats distributed in a field to reflect the solar radiation to a receiver installed at a tower. The large distances between the heliostats and their aim point require a high precision of the heliostat control and drive system to efficiently concentrate the solar radiation and to allow for a safe and reliable plant operation. Precise calibration procedures are needed for the commissioning of open loop systems. Furthermore, the need for cost reduction in the heliostat field motivates for precise and fast calibration systems allowing for savings in the design of the individual heliostats. This work introduces a method for the measurement of true aim points of heliostats in operation. A proposed application is the plug-in system HelioControl, to be used for parallel in-situ calibration of multiple heliostats in operation. The approach aims at extracting the focal spot position of individual heliostats on the receiver domain using image analysis and signal modulation. A centralized remote vision system, installed far from the harsh conditions at the focal area, takes image sequences of the receiver domain. During the measurement, a signal is modulated on different heliostats which is then extracted for the retrieval of the true aim points. This paper describes the theoretical background of the methodology and demonstrates the functionality based on two simulated cases showing the practical advantages introduced with this approach.
Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallelimageprocessing and load balancing under the circumstance ...
详细信息
ISBN:
(数字)9781728183640
ISBN:
(纸本)9781728183657
Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallelimageprocessing and load balancing under the circumstance of multi-tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer.
暂无评论