ArticleSensors (Basel, Switzerland)2023
Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Fractal dimension-based multi-focus image fusion via distance-weighted regional energy in curvelet domain.Scientific reports · 2026Article
- MicroDeblurNet: high-fidelity restoration of spatially variant defocus in microscopic images for cucumber downy mildew.Plant methods · 2026Article
- EDMF: A New Benchmark for Multi-Focus Images with the Challenge of Exposure Difference.Sensors (Basel, Switzerland) · 2024Article
- Robust Optimization Research of Cyber-Physical Power System Considering Wind Power Uncertainty and Coupled Relationship.Entropy (Basel, Switzerland) · 2024Article
- FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion.Sensors (Basel, Switzerland) · 2024Article
- Identification of Critical Links Based on Electrical Betweenness and Neighborhood Similarity in Cyber-Physical Power Systems.Entropy (Basel, Switzerland) · 2024Article
- An improved graph factorization machine based on solving unbalanced game perception.Frontiers in neurorobotics · 2024Article
- Real-time fault detection for IIoT facilities using GA-Att-LSTM based on edge-cloud collaboration.Frontiers in neurorobotics · 2024Article
- Analytic Design Technique for 2D FIR Circular Filter Banks and Their Efficient Implementation Using Polyphase Approach.Sensors (Basel, Switzerland) · 2023Article
- Multi-Domain Rapid Enhancement Networks for Underwater Images.Sensors (Basel, Switzerland) · 2023Article
- Micro-Doppler Effect and Sparse Representation Analysis of Underwater Targets.Sensors (Basel, Switzerland) · 2023Article
- Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
Multi-focus image fusion plays an important role in the application of computer vision. In the process of image fusion, there may be blurring and information loss, so it is our goal to obtain high-definition and information-rich fusion images. In this paper, a novel multi-focus image fusion method via local energy and sparse representation in the shearlet domain is proposed. The source images are decomposed into low- and high-frequency sub-bands according to the shearlet transform. The low-frequency sub-bands are fused by sparse representation, and the high-frequency sub-bands are fused by local energy. The inverse shearlet transform is used to reconstruct the fused image. The Lytro dataset with 20 pairs of images is used to verify the proposed method, and 8 state-of-the-art fusion methods and 8 metrics are used for comparison. According to the experimental results, our method can generate good performance for multi-focus image fusion.
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