Bispectral stellar image reconstruction: Image truncation, dynamic range, and object complexity

1995 ◽  
Vol 107 ◽  
pp. 77
Author(s):  
G. J. M. Aitken ◽  
R. Johnson ◽  
J. Meng
Keyword(s):  
Image Reconstruction ◽  
Dynamic Range ◽  
Reconstruction Image ◽  
Stellar Image

Image Reconstruction Based on Compressed Sensing with Split Bregman Algorithm and Fuzzy Bases

2012 ◽  
Vol 508 ◽  
pp. 80-83
Author(s):  
Jian Jiang Cui ◽  
Xu Jia ◽  
Jing Liu ◽  
Qi Li
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Reconstruction Algorithm ◽  
Original Data ◽  
Convergence Speed ◽  
Split Bregman ◽  
Reconstruction Image ◽  
Accelerate Convergence ◽  
Split Bregman Algorithm ◽  
Orthogonal Wavelet Transform

When original data is not complete or image degenerates, image reconstruction and recovery will be very important. In order to acquire reconstruction or recovery image with good quality, compressed sensing provides the possibility of achieving, and an image reconstruction algorithm based on compressed sensing with split Bregman method and fuzzy bases sparse representation is proposed, split strategy is applied in split Bregman algorithm in order to accelerate convergence speed; At the same time, discrete cosine transform and dual orthogonal wavelet transform are treated as bases to represent image sparsely, and image is reconstructed by using split Bregman algorithm. Experiments show that the proposed algorithm can improve convergence speed and reconstruction image quality.

Ultrasound-guided photoacoustic image reconstruction: image completion and boundary suppression

2013 ◽  
Vol 18 (9) ◽  
pp. 096017 ◽  
Author(s):  
Pieter Kruizinga ◽  
Frits Mastik ◽  
Dion Koeze ◽  
Nico de Jong ◽  
Anton F. W. van der Steen ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Image Completion ◽  
Ultrasound Guided ◽  
Reconstruction Image

Stellar image reconstruction from phase differences

1995 ◽  
Vol 118 (5-6) ◽  
pp. 466-472 ◽  
Author(s):  
Noriaki Miura ◽  
Naoki Sawai ◽  
Naoshi Baba
Keyword(s):  
Image Reconstruction ◽  
Stellar Image ◽  
Phase Differences

MAP Based Super-Resolution Image Reconstruction Method

2012 ◽  
Vol 220-223 ◽  
pp. 2754-2757
Author(s):  
Pan Li He ◽  
Bo Yang Wang ◽  
Xiao Xia Liu ◽  
Xiao Wei Han
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Registration Accuracy ◽  
Resolution Image ◽  
Research Fields ◽  
Reconstruction Image ◽  
Active Research ◽  
High Resolution Reconstruction

Super-resolution image reconstruction has been one of the most active research fields in recent years. In this paper, a new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low- resolution images that have been sub sampled. In the image registration, the paper puts forward an improved search strategies improving registration accuracy. In the MAP algorithm, the threshold parameters of solving the optimal value, making the estimated value of the optimal high-resolution images, so that the reconstructed image is better. The results of the experiments indicate that the proposed algorithm can not only make an automatic choice of the parameter and get the high resolution reconstruction image expected, but also can preserve the edges and details of the image effectively.

Sign in / Sign up

Export Citation Format

Share Document