The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Simultaneous image fusion and denoising with adaptive sparse representation
Open accessAbstract
In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR‐based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub‐dictionaries are learned from numerous high‐quality image patches which have been pre‐classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub‐dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi‐focus and multi‐modal image sets demonstrate that the ASR‐based fusion method can outperform the conventional SR‐based method in terms of both visual quality and objective assessment.
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-citationDistinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
Sparse Representation for Color Image Restoration
Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716
Cited by
Multilevel features convolutional neural network for multifocus image fusion
Yang, Y., Nie, Z., Huang, S., Lin, P., & Wu, J. (2019) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2018.2889959 · Cited by 103
DM-Fusion: Deep Model-Driven Network for Heterogeneous Image Fusion
Xu, G., He, C., Wang, H., Zhu, H., & Ding, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3238511 · Cited by 57
Xu, S., Ji, L., Wang, Z., Li, P., Sun, K., Zhang, C., & Zhang, J. (2020) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2020.3039564 · Cited by 47
Self-citation
Metadata
- Authors
- Liu, Y., & Wang, Z.
- Venue
- IET Image Processing
- Published
- 2015-05
- Volume
- 9
- Issue
- 5
- Pages
- 347-357
- ISSN
- 1751-9659 (ISSN-L)
- 1751-9667
- OpenAlex
- W1997480692
- Cited by
- 351
- References
- 5
- FWCI
- 12.78
- Type
- journal article
- Language
- en
- Open access
- bronze
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: Simultaneous image fusion and denoising with adaptive sparse representation. https://app.cochat.ai/papers/10.1049/iet-ipr.2014.0311
- The paper
- Liu, Y., & Wang, Z. (2015). Simultaneous image fusion and denoising with adaptive sparse representation. IET Image Processing. https://doi.org/10.1049/iet-ipr.2014.0311
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.
