Current Location:home > Browse

Institution

Your conditions: Computer Science(1,351)

1. chinaXiv:202008.00091 [pdf]

Better Than Reference In Low Light Image Enhancement Conditional Re-Enhancement Networks.pdf

张雨; 遆晓光; 张斌; 季锐航; 王春晖
Subjects: Computer Science >> Computer Application Technology

Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low light image enhancement method that can combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low light images as input and the enhanced V channel as condition, then it can re-enhance the contrast and brightness of the low light image and at the same time reduce noise and color distortion. It should be noted that during the training process, any paired images with different exposure time can be used for training, and there is no need to carefully select the supervised images which will save a lot. In addition, it takes less than 20 ms to process a color image with the resolution 400*600 on a 2080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness. (Code will be available at https://github.com/hitzhangyu/image-enhancement-with-denoise)

submitted time 2020-08-26 Hits1456Downloads162 Comment 0

2. chinaXiv:202008.00062 [pdf]

利用一种新的类脑人工神经网络实现恐惧学习和经典条件反射学习

位东涛; 王国清
Subjects: Psychology >> Physiological Psychology

神经科学对人工智能有很大的启发作用,通过借鉴上述学科的研究成果,我们设计了一种新的人工神经网络来对人脑内的杏仁核进行模拟。人工神经网络包含长时记忆网络和激活网络两个部分,记忆网络记录了发送信号和接收信号的神经元以及二者之间的权重,激活网络则记录了发送信号和接收信号的神经元及其信号发送的时间点。激活网络仅保留了事件发生时的一小段记忆,并会根据设定好的规则修改长时记忆网络中的权重。利用此类方法,我们成功地让智能体拥有了恐惧情绪学习和经典条件反射式学习的能力,这与生物体内杏仁核的功能非常类似。

submitted time 2020-08-07 Hits3045Downloads280 Comment 1

3. chinaXiv:202007.00047 [pdf]

鲁棒模式识别研究进展

张煦尧; 刘成林
Subjects: Computer Science >> Other Disciplines of Computer Science

目前诸多模式识别任务的识别精度获得不断提升,在一些任务上甚至超越了人的水平。单从识别精度的角度来看,模式识别似乎已经是一个被解决了的问题。然而,高精度的模式识别系统在实际应用中依旧会出现不稳定和不可靠的现象。因此,开放环境下的鲁棒性成为制约模式识别技术发展的新瓶颈。实际上,在大部分模式识别模型和算法背后蕴含着三个基础假设:封闭世界假设、独立同分布假设、以及大数据假设。这三个假设直接或间接影响了模式识别系统的鲁棒性,并且是造成机器智能和人类智能之间差异的主要原因。本文简要论述如何通过打破三个基础假设来提升模式识别系统的鲁棒性。

submitted time 2020-07-29 Hits3452Downloads470 Comment 0

4. chinaXiv:202007.00035 [pdf]

PandaDB:一种面向异构数据的智能融合管理系统

沈志宏; 赵子豪; 王华进; 刘忠新; 胡川; 周园春
Subjects: Computer Science >> Computer Application Technology

随着大数据应用的不断深入,大规模结构化、非结构化数据带来的异构数据的融合管理、关联计算和即席查询需求日益突出。现有异构数据融合管理技术与系统存在着数据模型表示能力弱、查询执行实时性差等问题。本文提出了适用于结构化、非结构化数据融合管理和语义计算的智能属性图模型,并定义了相关属性操作符和查询语法。基于该模型实现了异构数据融合管理系统PandaDB,并详细介绍了PandaDB的总体架构、存储机制、查询机制、属性协存、AI算法调度和分布式架构。测试实验和案例证明,PandaDB的协存机制和分布式架构具备较好的性能加速效果,并可应用在关联数据发布、个人相册管理、学术图谱实体消歧等融合数据智能管理的场景。

submitted time 2020-07-20 Hits2721Downloads287 Comment 0

5. chinaXiv:202006.00176 [pdf]

Automated Radiological Impression Generation for Plain Chest X-rays with End to End Deep Learning

Zhang, Shuai; Xin, Xiaoyan; Shen, Jingtao; Guo, Yachong; Wang, Yang; Yang, Xianfeng; Wang, Jun; Zhang, Jian; Zhang, Bing
Subjects: Computer Science >> Other Disciplines of Computer Science

The chest X-Ray (CXR) is the one of the most common clinical exam used to diagnose thoracic diseases and abnormalities. The volume of CXR scans generated daily in hospitals is huge. Therefore, an automated diagnosis system that is able to save the effort of doctors is of great value. At present, the applications of artificial intelligence in CXR diagnosis usually use pattern recognition to classify the scans. However, such methods rely on labeled databases. They are costly and usually have a high error rate. In this work, we built a database containing more than 12,000 CXR scans and radiological reports, and developed a model based on deep convolutional neural network and recurrent network with attention mechanism. The model learns features from the CXR scans and the associated raw radiological reports directly; no additional labeling required. The model provides automated recognition of given scans and generation of impression. The quality of the generated impression was evaluated with both the CIDEr scores and by radiologists as well. The CIDEr scores were found to be around 5.8 on average for the testing dataset. Further blind evaluation suggested a comparable performance against radiologists.

submitted time 2020-06-09 Hits6051Downloads335 Comment 0

6. chinaXiv:202004.00026 [pdf]

Learning an Adaptive Model for Extreme Low-light Raw Image Processing

付清旭; 遆晓光; 张雨1
Subjects: Computer Science >> Computer Application Technology

Low-light images suffer from severe noise and low illumination. Current deep learning models that are trained with real-world images have excellent noise reduction, but a ratio parameter must be chosen manually to complete the enhancement pipeline. In this work, we propose an adaptive low-light raw image enhancement network to avoid parameter-handcrafting and to improve image quality. The proposed method can be divided into two sub-models: Brightness Prediction (BP) and Exposure Shifting (ES). The former is designed to control the brightness of the resulting image by estimating a guideline exposure time t 1 . The latter learns to approximate an exposure-shifting operator ES, converting a low-light image with real exposure time t 0 to a noise-free image with guideline exposure time t 1 . Additionally, structural similarity (SSIM) loss and Image Enhancement Vector (IEV) are introduced to promote image quality, and a new Campus Image Dataset (CID) is proposed to overcome the limitations of the existing datasets and to supervise the training of the proposed model. In quantitative tests, it is shown that the proposed method has the lowest Noise Level Estimation (NLE) score compared with BM3D-based low-light algorithms, suggesting a superior denoising performance. Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene. Lastly, the potential application in video processing is briefly discussed.

submitted time 2020-04-14 Hits8907Downloads629 Comment 0

7. chinaXiv:202004.00007 [pdf]

完整的嗅觉神经通路假设及建模

张锦; 田恬恬
Subjects: Computer Science >> Computer Application Technology

嗅觉神经通路研究是嗅觉研究的基础,对脑科学研究同样具有多方面的重要意义。综合已有相关研究成果,探索了完整的嗅觉神经通路假设。该神经通路包括以嗅球层为核心的前端部分、以内嗅皮质为核心的中端部分和以齿状回为核心的后端部分。在此基础上,本文构建了完整的嗅觉神经通路结构模型,并进行了初步分析。

submitted time 2020-04-03 Hits12506Downloads617 Comment 0

8. chinaXiv:202004.00006 [pdf]

一种新的结合仿生学的人工神经网络模型评估研究.pdf

张锦; 舒炫煜; 黄昭彦; 易胜
Subjects: Computer Science >> Other Disciplines of Computer Science

人工神经网络的模型结构与功能分别朝着多样化、智能化趋势发展,但研究者仅从解决问题结果的优劣对模型进行评估是有所欠缺、过于片面的。因此在本文中提出从仿生学的角度构建评估人工神经网络仿生度的指标集,采用定性与定量的方式对模型的仿生度进行整体分析。在定性方面,对模型的神经元方程、网络结构、权重更新原理等方面进行比较分析;在定量方面,基于仿生的角度构建指标集即小世界特性、同步特性及混沌特性,对模型进行分析,分析结果表明,LeNet5模型及BP神经网络具备同步特性,但其与真实生物神经网络仍有一定的距离,而KIII模型在结构上具备一定的小世界特性,其网络内部也表现同步特性及混沌特性,与真实的生物神经网络更为接近。

submitted time 2020-03-29 Hits10328Downloads880 Comment 0

9. chinaXiv:202003.00049 [pdf]

基于自我介绍视频的人格预测技术研究

温业业; 陈德元; 李保滨; 汪晓阳; 刘晓倩; 朱廷劭
Subjects: Psychology >> Applied Psychology

人格影响着个体的工作生活方式,对于个体的心理疏导、职业发展等具有重要指导意义。传统方法通过量表测评人格得分存在个体拒绝回答、盲目作答等问题,近年来随着机器学习的发展为人格识别提供了新的思路。本文使用被试者自我介绍视频和大五人格量表得分,经过关键点提取、特征降维、建模、迭代调参等步骤,针对不同人格维度得到不同的预测模型。测试结果表明,基于自我介绍视频的人格预测模型在各维度都接近或达到中等相关,能够提供无侵扰的人格自动识别,为人格测量提供了新的思路。

submitted time 2020-03-08 Hits18916Downloads1159 Comment 0

10. chinaXiv:202003.00048 [pdf]

自监督图像增强网络:仅需低照度图像进行训练

张雨; 遆晓光; 张斌; 王春晖
Subjects: Computer Science >> Other Disciplines of Computer Science

本文提出了一种基于深度学习的自监督低照度图像增强方法。受信息熵理论和Retinex模型的启发,我们提出了一种基于信息熵最大的Retinex模型。利用该模型,一个非常简单的网络可以将照度图和反射图分离开来,且仅用低照度图像就可以进行训练。为了实现自监督学习,我们在模型中引入了一个约束条件:反射图的最大值通道与低照度图像的最大值通道一致,且其熵最大。我们的模型非常简单,不依赖任何精心设计的数据集(即使是一张低照度图像也能完成网络的训练),网络仅需进行分钟级的训练即可实现图像增强。实验证明,该方法在处理速度和效果上均达到了当前最新水平。

submitted time 2020-03-06 Hits16929Downloads797 Comment 1

12345678910  Last  Go  [136 Pages/ 1351 Totals]