Multi scale detail-preserving denoising method of infrared image via relative total variation

2013 ◽  
Author(s):  
Guang-mang Cui ◽  
Hua-jun Feng ◽  
Zhi-hai Xu ◽  
Qi Li ◽  
Yue-ting Chen
Keyword(s):  
Total Variation ◽  
Infrared Image ◽  
Denoising Method ◽  
Multi Scale ◽  
Relative Total Variation

scholarly journals Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification

2021 ◽  
Vol 13 (6) ◽  
pp. 1143
Author(s):  
Yinghui Quan ◽  
Yingping Tong ◽  
Wei Feng ◽  
Gabriel Dauphin ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Total Variation ◽  
Structure Analysis ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Heterogeneous Data ◽  
Lidar Data ◽  
Fusion Method ◽  
Wide Range ◽  
Relative Total Variation

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.

scholarly journals Performance Evaluation of Dorsal Vein Network of Hand Imaging Using Relative Total Variation-Based Regularization for Smoothing Technique in a Miniaturized Vein Imaging System: A Pilot Study

2021 ◽  
Vol 18 (4) ◽  
pp. 1548 ◽  
Author(s):  
Kyuseok Kim ◽  
Hyun-Woo Jeong ◽  
Youngjin Lee
Keyword(s):  
Image Quality ◽  
Total Variation ◽  
Near Infrared ◽  
Imaging System ◽  
Histogram Equalization ◽  
Evaluation Performance ◽  
Visual Evaluation ◽  
Évaluation Performance ◽  
Nir Imaging ◽  
Relative Total Variation

Vein puncture is commonly used for blood sampling, and accurately locating the blood vessel is an important challenge in the field of diagnostic tests. Imaging systems based on near-infrared (NIR) light are widely used for accurate human vein puncture. In particular, segmentation of a region of interest using the obtained NIR image is an important field, and research for improving the image quality by removing noise and enhancing the image contrast is being widely conducted. In this paper, we propose an effective model in which the relative total variation (RTV) regularization algorithm and contrast-limited adaptive histogram equalization (CLAHE) are combined, whereby some major edge information can be better preserved. In our previous study, we developed a miniaturized NIR imaging system using light with a wavelength of 720–1100 nm. We evaluated the usefulness of the proposed algorithm by applying it to images acquired by the developed NIR imaging system. Compared with the conventional algorithm, when the proposed method was applied to the NIR image, the visual evaluation performance and quantitative evaluation performance were enhanced. In particular, when the proposed algorithm was applied, the coefficient of variation was improved by a factor of 15.77 compared with the basic image. The main advantages of our algorithm are the high noise reduction efficiency, which is beneficial for reducing the amount of undesirable information, and better contrast. In conclusion, the applicability and usefulness of the algorithm combining the RTV approach and CLAHE for NIR images were demonstrated, and the proposed model can achieve a high image quality.

Improved multi-scale Retinex infrared image enhancement

2021 ◽  
Vol 36 (3) ◽  
pp. 465-474
Author(s):  
Ran-ran WEI ◽  
◽  
Wei-da ZHAN ◽  
De-peng ZHU ◽  
Yong TIAN
Keyword(s):  
Image Enhancement ◽  
Infrared Image ◽  
Multi Scale

Multi-scale saliency measure and orthogonal space for visible and infrared image fusion

2021 ◽  
Vol 118 ◽  
pp. 103916
Author(s):  
Yaochen Liu ◽  
Lili Dong ◽  
Wei Ren ◽  
Wenhai Xu
Keyword(s):  
Image Fusion ◽  
Infrared Image ◽  
Multi Scale ◽  
Orthogonal Space

Multi-scale non-local denoising method in neuroimaging

2016 ◽  
Vol 24 (3) ◽  
pp. 477-487
Author(s):  
Yiping Chen ◽  
Cheng Wang ◽  
Liansheng Wang
Keyword(s):  
Denoising Method ◽  
Multi Scale ◽  
Non Local

Multi-sensor image super-resolution with fuzzy cluster by using multi-scale and multi-view sparse coding for infrared image

2017 ◽  
Vol 76 (23) ◽  
pp. 24871-24902 ◽  
Author(s):  
Xiaomin Yang ◽  
Wei Wu ◽  
Kai Liu ◽  
Weilong Chen ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Infrared Image ◽  
Super Resolution ◽  
Fuzzy Cluster ◽  
Multi Scale ◽  
Image Super Resolution
Sign in / Sign up

Export Citation Format

Share Document