Image Fusion: Algorithms and ApplicationsThe growth in the use of sensor technology has led to the demand for image fusion: signal processing techniques that can combine information received from different sensors into a single composite image in an efficient and reliable manner. This book brings together classical and modern algorithms and design architectures, demonstrating through applications how these can be implemented. Image Fusion: Algorithms and Applications provides a representative collection of the recent advances in research and development in the field of image fusion, demonstrating both spatial domain and transform domain fusion methods including Bayesian methods, statistical approaches, ICA and wavelet domain techniques. It also includes valuable material on image mosaics, remote sensing applications and performance evaluation. This book will be an invaluable resource to R&D engineers, academic researchers and system developers requiring the most up-to-date and complete information on image fusion algorithms, design architectures and applications.
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From inside the book
Results 1-5 of 83
Algorithms and Applications Tania Stathaki. Preface. The need for Image Fusion in current image ... Images which have been acquired using different sensor modalities exhibit ... input sensors to a final composite image is the goal of a fusion ...
... images to be fused is a power of 2 [30] or they require that the images to be fused are in the same spectral domain [31]. Furthermore, most of the current data fusion methods do not properly preserve the spectral information of the input ...
... image from a spectral point of view. In general, this kind of assessments are not straightforward because the quality of the fused images depends on many factors like the difference in spatial or spectral resolution of the input images ...
... input images [41]. The spectral and the spatial ERGAS indices [42] were used to assess the quality of the fused images at the MERIS and the TM spatial resolutions. Bearing in mind that any fused image should be as identical as possible ...
... image fusion by the fusion community. Using various fusion rules, one can combine the important features of the input images in the transform domain to compose an enhanced image. In this study, the authors demonstrate the efficiency of ...
Contents
1 | |
27 | |
67 | |
85 | |
Chapter 5 Statistical modelling for waveletdomain image fusion | 119 |
Chapter 6 Theory and implementation of image fusion methods based on the á trous algorithm | 139 |
Chapter 7 Bayesian methods for image fusion | 157 |
Chapter 8 Multidimensional fusion by image mosaics | 193 |
Chapter 12 Enhancement of multiple sensor images using joint image fusion and blind restoration | 299 |
Chapter 13 Empirical mode decomposition for simultaneous image enhancement and fusion | 327 |
Chapter 14 Regionbased multifocus image fusion | 343 |
Chapter 15 Image fusion techniques for nondestructive testing and remote sensing applications | 367 |
Chapter 16 Concepts of image fusion in remote sensing applications | 393 |
Chapter 17 Pixellevel image fusion metrics | 429 |
Chapter 18 Objectively adaptive image fusion | 451 |
Chapter 19 Performance evaluation of image fusion techniques | 469 |
Chapter 9 Fusion of multispectral and panchromatic images as an optimisation problem | 223 |
Chapter 10 Image fusion using optimisation of statistical measurements | 251 |
Chapter 11 Fusion of edge maps using statistical approaches | 273 |
Subject index | 493 |