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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 Hits1404Downloads137 Comment 0

2. chinaXiv:202005.00010 [pdf]

浑善达克沙地杨树水分利用特征

苏文旭; 贾德彬; 冯蕴; 张雨强
Subjects: Environmental Sciences, Resource Sciences >> Basic Disciplines of Environmental Science and Technology

为探究浑善达克沙地杨树的水分利用特征。本文利用氢和氧同位素示踪技术,测定了降雨、土壤水与地下水的δ18O值,利用多元线性混合模型定量计算了杨树对不同土层土壤水分的利用比例。结果表明:① 浑善达克沙地大气降雨方程线为:δDLWML=7.84δ18OLWML+9.12,斜率比全国降雨方程偏小,体现了研究区降雨少,蒸发大的气候特征;② 土壤含水量与地下水位埋深、降雨量、植物生长期的变化有着显著的相关关系。降雨量较大与地下水位埋深较浅的时期,土壤含水量明显增大,在植物生长前期和中期,土壤含水量明显较低;③ 杨树在雨季,利用了大量的浅层土壤水(0~40 cm),在较为干旱的旱季,利用了大量的深层土壤(160~200 cm)水与少量的地下水。

submitted time 2020-04-26 From cooperative journals:《干旱区研究》 Hits288Downloads163 Comment 0

3. 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 Hits8875Downloads612 Comment 0

4. 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 Hits16880Downloads774 Comment 1

5. chinaXiv:201812.00072 [pdf]

多尺度自适应阈值局部三值模式编码算法

张雨; 王强; 李柏林; 高攀
Subjects: Computer Science >> Integration Theory of Computer Science

针对局部二值模式(local binary pattern,LBP)描述信息单一以及对噪声敏感的问题,提出一种多尺度自适应阈值局部三值模式(multi-scale adaptive local ternary pattern,MSALTP)编码算法。MSALTP首先将原始图像放大;其次将图像平均划分成几个区域,并计算像素的均值;然后计算每个区域中心像素与均值的偏差;最后提取ALTP特征,将结果统计特征直方图实现图像分类。实验表明提出的算法识别率比目前较好的抗噪声算法在不同的噪声下识别率有较大提高。

submitted time 2018-12-13 From cooperative journals:《计算机应用研究》 Hits874Downloads427 Comment 0

6. chinaXiv:201712.00411 [pdf]

移动医疗APP中关于肥胖和Ⅱ型糖尿病及代谢减重手术患教信息质量的调查

陈亿(1);袁祥(1);胡亦凡(2);李舍予(2);张雨薇(2);王覃(2;程中(1)
Subjects: Medicine, Pharmacy >> Preclinical Medicine

目的 调查中国移动医疗APP中关于肥胖和代谢减重手术患教信息的质量,总结现阶段移动医疗APP中关于肥胖和Ⅱ型糖尿病患教信息质量是否满足患者的需求。方法 在“Apple Store”和“应用宝”应用搜索栏输入关键字“医”,下载相关App共63个,筛选后共28个App用“Silberg scale”和“Abott scale”评测相关患教信息,再与国外移动医疗APP相关患教消息质量的调查结果做对比分析,初步评测现阶段国内APP在相关患教信息的质量。结果 在最终评测的28个APP的患教信息得分中,“Silberg scale”量表平均得分为2.96±1.27分,“Abott scale”量表平均得分为1.61±1.08分。内容质量评分为3.85±1.76分。结论 目前中国移动医疗App中关于肥胖和Ⅱ型糖尿病代谢减重手术患教信息在作者认证、信息来源、信息更新方面较国外APP差距较大,肥胖和Ⅱ型糖尿病外科治疗的患教信息质量较差,患教信息缺乏足够的准确性和权威性,信息内容不够全面,且缺乏及时的更新与修改。患教信息内容单一,对外科治疗的描述不够准确,缺乏在手术风险、并发症以及术后生活方式的介绍。

submitted time 2017-12-07 From cooperative journals:《分子影像学杂志》 Hits488Downloads260 Comment 0

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