The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy
Abstract
Multi-focus image fusion (MFF) is a popular technique to generate an all-in-focus image, where all objects in the scene are sharp. However, existing methods pay little attention to defocus spread effects of the real-world multi-focus images. Consequently, most of the methods perform badly in the areas near focus map boundaries. According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects. Firstly, a new MFF assessment metric is presented by combining the principle of structure similarity and detected focus maps. Then, MFF problem is cast into maximizing this metric. The optimization is solved by gradient ascent. Experiments conducted on the real-world dataset verify superiority of the proposed model. The codes are available athttps://github.com/xsxjtu/MFF-SSIM.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Self-citationRobust Face Recognition via Sparse Representation
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,388
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
FSIM: A Feature Similarity Index for Image Quality Assessment
Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2109730 · Cited by 5,203
Objective image fusion performance measure
Xydeas, C. S., & Petrović, V. (2000) · Electronics Letters · doi:10.1049/el:20000267 · Cited by 1,996
Image Fusion With Guided Filtering
Li, S., Kang, X., & Hu, J. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2244222 · Cited by 1,781
IFCNN: A general image fusion framework based on convolutional neural network
Zhang, Y., Liu, Y., Sun, P., Yan, H., Zhao, X., & Zhang, L. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.07.011 · Cited by 1,550
A general framework for image fusion based on multi-scale transform and sparse representation
Liu, Y., Liu, S., & Wang, Z. (2015) · Information Fusion · doi:10.1016/j.inffus.2014.09.004 · Cited by 1,431
Self-citationA wavelet-based image fusion tutorial
Pajares, G., & Manuel de la Cruz, J. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2004.03.010 · Cited by 1,328
Multi-focus image fusion with a deep convolutional neural network
Liu, Y., Chen, X., Peng, H., & Wang, Z. (2017) · Information Fusion · doi:10.1016/j.inffus.2016.12.001 · Cited by 1,327
Self-citation
Metadata
- Authors
- Xu, S., Ji, L., Wang, Z., Li, P., Sun, K., Zhang, C., & Zhang, J.
- Venue
- IEEE Transactions on Computational Imaging
- Published
- 2020
- Volume
- 6
- Pages
- 1561-1570
- ISSN
- 2333-9403
- 2334-0118
- 2573-0436
- Cited by
- 47
- References
- 36
- Licence
- Funders
- National Key Research and Development Program of China (2018AAA0102201)
- National Natural Science Foundation of China (11671317, 61976174)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy. https://app.cochat.ai/papers/10.1109/tci.2020.3039564
- The paper
- Xu, S., Ji, L., Wang, Z., Li, P., Sun, K., Zhang, C., & Zhang, J. (2020). Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/tci.2020.3039564
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
