中国科学院机构知识库网格系统: 检索

CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning 期刊论文 OAI收割
Sensors, 2024, 卷号: 24, 期号: 1
作者: Guo, Yi; Gao, Yuanhang; Hu, Bingliang; Qian, Xueming; Liang, Dong

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收藏 | 浏览/下载：4/0 | 提交时间：2024/02/07

deep reinforcement learning pixel-wise image processing crossmodal image denoising infrared image denoising terahertz image denoising

Infrared star image denoising using regions with deep reinforcement learning 期刊论文 OAI收割
Infrared Physics and Technology, 2021, 卷号: 117
作者: Z. Zhang; W. Zheng; Z. Ma; L. Yin; M. Xie and Y. Wu

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收藏 | 浏览/下载：7/0 | 提交时间：2022/06/13

Infrared stripe correction algorithm based on wavelet decomposition and total variation-guided filtering 期刊论文 OAI收割
JOURNAL OF THE EUROPEAN OPTICAL SOCIETY-RAPID PUBLICATIONS, 2020, 卷号: 16, 期号: 1, 页码: 1-12
作者: Wang ED(王恩德); Jiang P(姜平); Li XP(李学鹏); Cao H(曹晖)

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收藏 | 浏览/下载：14/0 | 提交时间：2020/01/11

Focal-plane array non-uniformity correction Infrared image Multi-scale Destriping

Super-resolution imaging for infrared microscanning optical system 期刊论文 OAI收割
Optics Express, 2019, 卷号: 27, 期号: 5, 页码: 7719-7737
作者: X.F.Zhang; W.Huang; M.F.Xu; S.Q.Jia; X.R.Xu

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收藏 | 浏览/下载：29/0 | 提交时间：2020/08/24

Optical systems,Frequency domain analysis,Image denoising,Image reconstruction,Optical resolving power,Pixels,Scanning,Set theory,Textures,Thermography (imaging)

Image enhancement for the observation of the solar corona 会议论文 OAI收割
Austin, TX, 2018-06-11
作者: Zhao MY(赵明宇); Liu Y(刘煜); Song TF(宋腾飞); Zhang XF(张雪飞); Hagino, M.

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收藏 | 浏览/下载：24/0 | 提交时间：2018/12/27

Sun: Corona Techniques: Image Processing

Gradient sky scene based nonuniformity correction and local weighted filter based denoising 期刊论文 OAI收割
Optik, 2018, 卷号: 174, 页码: 748-756
作者: Liu, R.; Wang, D. J.; Jia, P.; Che, X.

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收藏 | 浏览/下载：7/0 | 提交时间：2019/09/17

Nonuniformity correction Gradient sky scene Local weighted filter Residual isolated noise focal-plane arrays sensors Optics

Gaussian mixture model-based gradient field reconstruction for infrared image detail enhancement and denoising 期刊论文 OAI收割
Infrared Physics & Technology, 2016, 卷号: 76
作者: Zhao, F.; J. Zhao; W. D. Zhao and F. Qu

收藏 | 浏览/下载：14/0 | 提交时间：2017/09/11

一种自适应非均匀性校正算法 期刊论文 OAI收割
计算机辅助设计与图形学学报, 2016, 卷号: 28, 期号: 1, 页码: 138-145
作者: 于世孔; 向伟; 赵耀宏; 张程硕

收藏 | 浏览/下载：55/0 | 提交时间：2016/01/30

非均匀性校正神经网络鬼影三边滤波

Suppression of infrared complex background based on adaptive gradient threshold anisotropic filtering 期刊论文 OAI收割
Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2014, 卷号: 22, 期号: 1, 页码: 146-151
作者: Wang Y.-J.

收藏 | 浏览/下载：15/0 | 提交时间：2015/04/24

A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE) 会议论文 OAI收割
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
作者: Li Y.; Li Y.; Li Y.; Li Y.

收藏 | 浏览/下载：25/0 | 提交时间：2013/03/25

This paper proposed a new approach for the removal of mixed noise. There are many different ways in image denoising. Donoho et al have proposed a method for image de-noising by thresholding ambiguity is often resulted in determining the correspondence of a modulus maximum to a singularity. In the light and indeed we combine the merits of the two techniques to form a new approach for the removal of mixed noise. At first the application of their method to image denoising has been extremely successful. But the method of Donoho is based on the assumption that the type of noise is only additive Gaussian noise we used wavelet singularity detection (WSD) technique to analyze singularities of signal and noise. According to the characteristic that wavelet transform modulus maxima of impulse noise rapidly decreases as the scale increases in wavelet domain which is not successful for impulse noise. Mallat has also presented a method for signal denoising by discriminating the noise and the signal singularities through an analysis of their wavelet transform modulus maxima (WTMM). Nevertheless it can be accurately located with multiscale space by going through dyadic orthogonal wavelet transform and removed. Furthermore the Gaussian noise is also removed through a level-dependent thresholding algorithm the tracing of WTMM is not just tedious procedure computationally algorithm. The experimental results demonstrate that the proposed method can effectively detect impulse noise and remove almost all of the noise while preserve image details very well.