Image fusion: Advances in the state of the art

By A. Goshtasby, Stavri G. Nikolov. Information Fusion, 2007

607 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/53dcfbd671a3794a56232c4d29620731ed657f55

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Shutao Li
2. Gustau Camps-Valls
3. Luis Gómez-Chova
4. Gabriele Moser
5. Mohammad Haghighat
6. Bin Yang
7. Xudong Kang
8. Ali Aghagolzadeh

In Media Technology

1. Shutao Li
2. Xudong Kang
3. Jianwen Hu
4. Yu Liu
5. Devis Tuia
6. Gustau Camps-Valls
7. Luis Gómez-Chova
8. Bin Yang

Citations in the corpus

• Performance comparison of different multi-resolution transforms for image fusion (2011, methodology): “To obtain an image that simultaneously contains the outline of scene as well as special objects for the convenience of human visual perception or for further image-processing tasks [1], image fusion can be used to integrate the information provided by individual sensors.”
• Multisensor data fusion: A review of the state-of-the-art (2013, background): “While several general [1] [2] [3] and specific [4] [5] [6] [7] [8] reviews of the data fusion literature exist; this paper is intended to provide the reader with a generic and comprehensive view of contemporary data fusion methodologies, as well as the most recent developments and emerging trends…”
• Fusion Techniques for Reliable Information: A Survey (2010, background): “The main objective of image fusions is to minimize the uncertainty and the redundancy while maximizing the relevant information particular to applications [41].”
• A non-reference image fusion metric based on mutual information of image features (2011, background): “Using image fusion has several advantages, including wider spatial and temporal coverage, decreased uncertainty, improved reliability, and increased robustness of system performance [1].”
• Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, methodology): “A wide variety of image fusion methods have been proposed in the remote sensing literature [7], [17], [18], [38], [39], being pan-sharpening methods [24], [32], [35], [40] among the most widespread.”
• Opinion Mining and Information Fusion: A survey (2016, background): “The work in [154] presents the state of the art in image fusion.”
• Multifocus Image Fusion and Restoration With Sparse Representation (2010, methodology): “As a result, undesirable artifacts may be produced in the fusion process using the multiresolution transform-based methods in some cases [ 3 ].”