Super-resolution image processing with total variation regularization and shock filters

2012 ◽  
Author(s):  
Masashi Watanabe ◽  
Yasutaka Sakuta ◽  
Tomio Goto ◽  
Satoshi Hirano ◽  
Masaru Sakurai
Keyword(s):  
Image Processing ◽  
Total Variation ◽  
Super Resolution ◽  
Total Variation Regularization ◽  
Resolution Image ◽  
Shock Filters

Super-resolution image reconstruction based on Tukey data fusion and bilateral-total-variation regularization

2014 ◽  
Vol 21 (1) ◽  
pp. 35-42
Author(s):  
Yan Chen ◽  
Shuhua Wang ◽  
Weiqi Jin ◽  
Guangping Wang ◽  
Weili Chen ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Data Fusion ◽  
Total Variation ◽  
Super Resolution ◽  
Total Variation Regularization ◽  
Resolution Image ◽  
Bilateral Total Variation

Super-Resolution Analysis on Molten Pool Image of Metal Active-Gas Welding Based on Wavelet with Fractal

2011 ◽  
Vol 103 ◽  
pp. 152-157
Author(s):  
Guang Zhi Dai ◽  
Guo Qiang Han ◽  
Chao Yi Dong
Keyword(s):  
Image Processing ◽  
Super Resolution ◽  
Image Interpolation ◽  
Image Resolution ◽  
Mag Welding ◽  
Resolution Image ◽  
Fractal Image ◽  
Welding Image ◽  
Resolution Analysis ◽  
Wavelet Image

According to the unique advantages in image processing combining wavelet and fractal and the different ways of combination, a super-resolution image processing methods are proposed. The methods are characterized by combining the wavelet transform, Wavelet Image Interpolation and FBM Fractal Image interpolation in a certain way to achieve super-resolution image reconstruction. Through processing MAG welding pool images polluted by noises seriously, the results show that: the method proposed in this paper, compared with the method based on wavelet bilinear interpolation, not only effectively raises MAG welding image resolution, but also PSNR of reconstruction images are enhanced 21.1049 dB.

Notice of Removal: Automated super-resolution image processing in ultrasound using machine learning

2017 ◽  
Author(s):  
Kirsten Christensen Jeffries ◽  
Markus Schirmer ◽  
Jemma Brown ◽  
Sevan Harput ◽  
Meng-Xing Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Super Resolution ◽  
Resolution Image

scholarly journals TV2++: A Novel Spatial-Temporal Total Variation for Super Resolution with Exponential-type Norm

2020 ◽  
Author(s):  
Lizhen Deng ◽  
Zhetao Zhou ◽  
Guoxia Xu ◽  
Hu Zhu ◽  
Bing-Kun Bao
Keyword(s):  
Total Variation ◽  
Exponential Type ◽  
Optimization Problems ◽  
Super Resolution ◽  
Low Rank ◽  
Good Effect ◽  
Total Variation Regularization ◽  
Time Dimension ◽  
Tv Regularization ◽  
Image Super Resolution

Abstract Recently, many super-resolution algorithms have been proposed to recover high resolution images to improve visualization and help better analyze images. Among them, total variation regularization (TV) methods have been proven to have a good effect in retaining image edge information. However, these TV methods do not consider the temporal correlation between images. Our algorithm designs a new TV regularization (TV2++) to take advantage of the time dimension information of the images, further improving the utilization of useful information in the images. In addition, the union of global low rank regularization and TV regularization further enhances the image super resolution recovery. And we extend the exponential-type penalty (ETP) function on singular values of a matrix to enhance low-rank matrix recovery. A novel image super-resolution algorithm based on the ETP norm and TV2++ regularization is proposed. And the alternating direction method of multipliers (ADMM) is applied to solve the optimization problems effectively. Numerous experimental results prove that the proposed algorithm is superior to other algorithms.

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