scholarly journals Variational Osmosis for Non-Linear Image Fusion

2020 ◽  
Vol 29 ◽  
pp. 5507-5516
Author(s):  
Simone Parisotto ◽  
Luca Calatroni ◽  
Aurelie Bugeau ◽  
Nicolas Papadakis ◽  
Carola-Bibiane Schonlieb
Keyword(s):  
Image Fusion ◽  
Non Linear

scholarly journals Wavelet-based medical image fusion via a non-linear operator

2016 ◽  
Author(s):  
Zaid Omar ◽  
Saif S. Ahmed ◽  
Musa Mokji ◽  
Marsyita Hanafi ◽  
Vikrant Bhateja
Keyword(s):  
Linear Operator ◽  
Image Fusion ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Non Linear ◽  
Non Linear Operator

A cellular non-linear network for image fusion based on data regularization

2006 ◽  
Vol 34 (5) ◽  
pp. 533-546 ◽  
Author(s):  
Andrea Anzalone ◽  
Federico Bizzarri ◽  
Marco Storace ◽  
Mauro Parodi
Keyword(s):  
Image Fusion ◽  
Linear Network ◽  
Non Linear ◽  
Data Regularization

scholarly journals A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery

2020 ◽  
Vol 10 (9) ◽  
pp. 3298
Author(s):  
Dae Kyo Seo ◽  
Yang Dam Eo
Keyword(s):  
Image Fusion ◽  
Spatial Information ◽  
Sufficient Information ◽  
Random Forest Regression ◽  
Complementary Method ◽  
Linear Relationships ◽  
Source Data ◽  
Non Linear ◽  
Fusion Methods ◽  
Imaging Mechanisms

Image fusion is an effective complementary method to obtain information from multi-source data. In particular, the fusion of synthetic aperture radar (SAR) and panchromatic images contributes to the better visual perception of objects and compensates for spatial information. However, conventional fusion methods fail to address the differences in imaging mechanism and, therefore, they cannot fully consider all information. Thus, this paper proposes a novel fusion method that both considers the differences in imaging mechanisms and sufficiently provides spatial information. The proposed method is learning-based; it first selects data to be used for learning. Then, to reduce the complexity, classification is performed on the stacked image, and the learning is performed independently for each class. Subsequently, to consider sufficient information, various features are extracted from the SAR image. Learning is performed based on the model’s ability to establish non-linear relationships, minimizing the differences in imaging mechanisms. It uses a representative non-linear regression model, random forest regression. Finally, the performance of the proposed method is evaluated by comparison with conventional methods. The experimental results show that the proposed method is superior in terms of visual and quantitative aspects, thus verifying its applicability.

B. The Non-Linear Calculations for Pulsating Stars

1967 ◽  
Vol 28 ◽  
pp. 105-176
Author(s):  
Robert F. Christy
Keyword(s):  
Major Part ◽  
Afternoon Session ◽  
Discussion Session ◽  
Pulsating Stars ◽  
Fourth Symposium ◽  
Non Linear ◽  
Considerable Discussion ◽  
Informal Approach ◽  
Preceding Session

(Ed. note: The custom in these Symposia has been to have a summary-introductory presentation which lasts about 1 to 1.5 hours, during which discussion from the floor is minor and usually directed at technical clarification. The remainder of the session is then devoted to discussion of the whole subject, oriented around the summary-introduction. The preceding session, I-A, at Nice, followed this pattern. Christy suggested that we might experiment in his presentation with a much more informal approach, allowing considerable discussion of the points raised in the summary-introduction during its presentation, with perhaps the entire morning spent in this way, reserving the afternoon session for discussion only. At Varenna, in the Fourth Symposium, several of the summaryintroductory papers presented from the astronomical viewpoint had been so full of concepts unfamiliar to a number of the aerodynamicists-physicists present, that a major part of the following discussion session had been devoted to simply clarifying concepts and then repeating a considerable amount of what had been summarized. So, always looking for alternatives which help to increase the understanding between the different disciplines by introducing clarification of concept as expeditiously as possible, we tried Christy's suggestion. Thus you will find the pattern of the following different from that in session I-A. I am much indebted to Christy for extensive collaboration in editing the resulting combined presentation and discussion. As always, however, I have taken upon myself the responsibility for the final editing, and so all shortcomings are on my head.)

1528: Improved Sentinel Node Detection and Pre-Operative Planning in Prostate Cancer by Image Fusion of CT/MRI and Spect

2005 ◽  
Vol 173 (4S) ◽  
pp. 414-414
Author(s):  
Frank G. Fuechsel ◽  
Agostino Mattei ◽  
Sebastian Warncke ◽  
Christian Baermann ◽  
Ernst Peter Ritter ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Sentinel Node ◽  
Image Fusion ◽  
Sentinel Node Detection ◽  
Operative Planning

A decomposition method in non-linear programmm

Optimization ◽  
1975 ◽  
Vol 6 (4) ◽  
pp. 549-559
Author(s):  
L. Gerencsér
Keyword(s):  
Decomposition Method ◽  
Non Linear
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