A general framework for multiresolution image fusion: from pixels to regions

By Gemma Piella. Information Fusion, 2003

910 citations recorded by the source corpus.

Subfields: Media Technology

View Paper: https://www.semanticscholar.org/paper/8fe260b78d6974e062c3e6d1257a094886322e7e

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In Media Technology

1. Jiayi Ma
2. Shutao Li
3. Yu Liu
4. Zengfu Wang
5. Hui Li
6. Junjun Jiang
7. Leyuan Fang
8. Hao Zhang

Citations in the corpus

• Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “In the case of the liquid crystal itself there are so many protons and hence so many overlapping transitions that fine structure is no longer observed.”
• FusionGAN: A generative adversarial network for infrared and visible image fusion (2019, background): “…transform-based methods are the most popular in image fusion, and multi-scale transform can decompose original images into components of different scales, where each component represents the sub-image at each scale and real-world objects typically comprise components at different scales [23].”
• Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019, background): “Since there is strong evidence that the human visual system (HVS) processes information in a multiresolution fashion [14], most medical image fusion methods are introduced under a multiscale transform (MST)-based framework to pursue perceptually good results.”
• Simultaneous image fusion and denoising with adaptive sparse representation (2015, methodology): “The TDF methods share a ‘ decomposition-fusion-reconstruction ’ framework [7], namely, decompose the source images into a multi-scale domain, fuse the transformed coef fi cients by a given rule and reconstruct the fused image with the merged coef fi cients.”
• Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study (2012, background): “to be verified and collectively evaluated [2], [3].”
• Multi-focus image fusion with a deep convolutional neural network (2017, methodology): “These MST-based methods share a universal three-step framework, namely, decomposition, fusion and reconstruction [7] .”
• Pixel-based and region-based image fusion schemes using ICA bases (2007, methodology): “As we process each image in N × N patches, we can use the mean absolute value (L1-norm) of each patch (arranged in a vector) in the transform domain, as an activity indicator in each patch.”
• Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics (2008, background): “I. INTRODUCTION A LL IMAGING applications that require analysis of two ormore images of a scene can benefit from image fusion.”
• Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering (2012, neutral): “First, for the wavelet fusion approach that we proposed, the key idea is to restrain the background (unchanged areas) information and to enhance the information of changed regions in the greatest extent.”
• Pixel-level image fusion: A survey of the state of the art (2017, methodology): “…method[36], weighted-average[4, 37, 38], and substitution[12, 15] based coeffi-cients combining, window and region based consistency verification[5, 39], cross-scale fusion rule [40], guided filtering based weighted average [41] Sparse representation (SR) orthogonal matching pursuit[42, 43], group…”
• Multifocus Image Fusion and Restoration With Sparse Representation (2010, methodology): “morphological wavelet transform (MWT) [8]) and general multifocus image fusion methods (the DWT [1], [5], [6], the stationary wavelet transform (SWT) [7], [24], the CVT [29],”
• A new quality metric for image fusion (2003, methodology): “The fused image is obtained by applying the inverse transform on this composite MR representation [3].”
• A general framework for image fusion based on multi-scale transform and sparse representation (2015, methodology): “In general, the MST-based fusion methods consist of the following three steps [10].”