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Synopsis
Written in an easy-to-follow approach, the text will help the readers to understand the techniques and applications of image fusion for remotely sensed multi-spectral images. It covers important multi-resolution fusion concepts along with the state-of-the-art methods including super resolution and multi stage guided filters. It includes in depth analysis on degradation estimation, Gabor Prior and Markov Random Field (MRF) Prior. Concepts such as guided filter and difference of Gaussian are discussed comprehensively. Novel techniques in multi-resolution fusion by making use of regularization are explained in detail. It also includes different quality assessment measures used in testing the quality of fusion. Real-life applications and plenty of multi-resolution images are provided in the text for enhanced learning.
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About the Authors
Manjunath V. Joshi has received his M.Tech. and Ph.D. degrees from the Indian Institute of Technology (IIT) Bombay. Currently, he is serving as a Professor with the Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, India. He has been involved in active research in the areas of computer vision, machine learning and cognitive radio and has several publications in quality journals and conferences. He has co-authored two books entitled Motion-Free Super Resolution (2005) and Digital Heritage Reconstruction using Super-resolution and Inpainting (2016). Dr Joshi has received Outstanding Researcher Award in Engineering by the Research Scholars Forum of IIT Bombay and Dr Vikram Sarabhai Award for 2006–2007.
Kishor P. Upla is Assistant Professor in Sardar Vallabhbhai National Institute of Technology, Surat (SVNIT). He received his Ph.D. degree from Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, India. He has fourteen years of teaching experience. His areas of interest include signal and image processing, multi-spectral and hyperspectral image analysis.
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Multi-resolution Image Fusion in Remote Sensing
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