Image Fusion With Guided Filtering

By Shutao Li, Xudong Kang, Jianwen Hu. IEEE Transactions on Image Processing, 2013

1,641 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/4c01895ad7929885adacf03789a819848aa587d9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jiayi Ma
2. Hui Li
3. Xiaojun Wu
4. Josef Kittler
5. Han Xu
6. Junjun Jiang
7. Xiaojie Guo
8. Luc Van Gool

In Media Technology

1. Jiayi Ma
2. Hui Li
3. Xiaojun Wu
4. Han Xu
5. Josef Kittler
6. Yiannis Demiris
7. Xingchen Zhang
8. Junjun Jiang

Citations in the corpus

• Pixel-level image fusion: A survey of the state of the art (2017, methodology): “However, in recent years, researchers find that advanced fusion rules can address many limitations of the multi-scale transforms, and thus, providing state-of-the-art fusion performances [40, 41].”
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection (2022, background): “Traditional multi-scale transform [10, 24], optimization model [16, 20, 41], spare representation [37, 43], and subspace methods attempt to discover intrinsic common features of the two modalities and to design appropriate weighting rules for fusion.”
• DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion (2020, background): “For multi-modality source images, the key of image fusion is to extract the most important feature information in source images taken from different imaging apparatus and merge it into a single fused image [8].”
• Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019, methodology): “onal matching pursuit (SR-SOMP) method [4], the guided filtering (GF) method [11], the phase congruency and directive contrast in NSCT domain (NSCT-PCDC) method [22], the NSCT-RPCNN method [23], the LP-SR method [8], the CTD-SR method [28], the LLF-IOI method [29], and the LP-CNN method [30].”
• RFN-Nest: An end-to-end residual fusion network for infrared and visible images (2021, methodology): “In the traditional algorithm category, multi-scale transform methods [8][9][10][11] are widely applied to extract multi-scale features from the source images.”
• U2Fusion: A Unified Unsupervised Image Fusion Network (2020, methodology): “The local dark regions in GFF, DSIFT, and FLER are improved in U2Fusion.”
• FusionDN: A Unified Densely Connected Network for Image Fusion (2020, background): “…al. 2016), FPDE (Bavirisetti, Xiao, and Liu 2017), DenseFuse (Li and Wu 2018) and FusionGAN (Ma et al. 2019) for infrared and visible fusion, GFF (Li, Kang, and Hu 2013), DSIFT (Liu and Wang 2015), GBM (Paul, Sevcenco, and Agathoklis 2016), Deepfuse (Prab-hakar, Srikar, and Babu 2017) and FLER…”
• DATFuse: Infrared and Visible Image Fusion via Dual Attention Transformer (2023, methodology): “The basic fusion strategies include weighted averaging [40] and maximum selection [41].”
• FusionGAN: A generative adversarial network for infrared and visible image fusion (2019, methodology): “The third image is the fusion result by using a recent method [22].”
• PCA-Based Edge-Preserving Features for Hyperspectral Image Classification (2017, neutral): “decades [33]–[36].”
• Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering (2014, background): “Most of these EPFs have a similar property, i.e., they can be used for joint filtering, where the content of one image is smoothed based on the edge information from a guidance image.”
• Artificial intelligence in the creative industries: a review (2020, background): “Ma [193] shows that a guided filtering-based fusion [194] achieves the best results based on the visual information fidelity (VIF) metric, but proposed that fused images with very low correlation coefficients (CC), measuring the degree of linear correlation between the fused image its source images,”