ArticleSensors (Basel, Switzerland)2021
Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Fractal dimension-based multi-focus image fusion via distance-weighted regional energy in curvelet domain.Scientific reports · 2026Article
- Color Night Light Remote Sensing Images Generation Using Dual-Transformation.Sensors (Basel, Switzerland) · 2024Article
- Gaussian of Differences: A Simple and Efficient General Image Fusion Method.Entropy (Basel, Switzerland) · 2023Article
- Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain.Sensors (Basel, Switzerland) · 2023Article
- X-ray Image Enhancement Based on Nonsubsampled Shearlet Transform and Gradient Domain Guided Filtering.Sensors (Basel, Switzerland) · 2022Article
- Fusion of Infrared and Visible Images Using Fast Global Smoothing Decomposition and Target-Enhanced Parallel Gaussian Fuzzy Logic.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
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Abstract
The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.
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