Blob reconstruction using unilateral second order Gaussian kernels with application to high-ISO long-exposure image denoising
- Author
- Gang WANG, Carlos Lopez-Molina and Bernard De Baets (UGent)
- Organization
- Abstract
- Blob detection and image denoising are fundamental, and sometimes related, tasks in computer vision. In this paper, we propose a blob reconstruction method using scale-invariant normalized unilateral second order Gaussian kernels. Unlike other blob detection methods, our method suppresses non-blob structures while also identifying blob parameters, i.e., position, prominence and scale, thereby facilitating blob reconstruction. We present an algorithm for high-ISO long-exposure noise removal that results from the combination of our blob reconstruction method and state-of-the-art denoising methods, i.e., the non-local means algorithm (NLM) and the color version of block-matching and 3-D filtering (CBM3D). Experiments on standard images corrupted by real high-ISO long-exposure noise and real-world noisy images demonstrate that our schemes incorporating the blob reduction procedure outperform both the original NLM and CBM3D.
- Keywords
- GENERALIZED LAPLACIAN, SCALE SELECTION, ALGORITHMS, TRACKING, FILTERS, NUCLEI, NOISE
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8634309
- MLA
- WANG, Gang, et al. “Blob Reconstruction Using Unilateral Second Order Gaussian Kernels with Application to High-ISO Long-Exposure Image Denoising.” 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017, pp. 4827–35, doi:10.1109/iccv.2017.516.
- APA
- WANG, G., Lopez-Molina, C., & De Baets, B. (2017). Blob reconstruction using unilateral second order Gaussian kernels with application to high-ISO long-exposure image denoising. 2017 IEEE International Conference on Computer Vision (ICCV), 4827–4835. https://doi.org/10.1109/iccv.2017.516
- Chicago author-date
- WANG, Gang, Carlos Lopez-Molina, and Bernard De Baets. 2017. “Blob Reconstruction Using Unilateral Second Order Gaussian Kernels with Application to High-ISO Long-Exposure Image Denoising.” In 2017 IEEE International Conference on Computer Vision (ICCV), 4827–35. New York, NY, USA: IEEE. https://doi.org/10.1109/iccv.2017.516.
- Chicago author-date (all authors)
- WANG, Gang, Carlos Lopez-Molina, and Bernard De Baets. 2017. “Blob Reconstruction Using Unilateral Second Order Gaussian Kernels with Application to High-ISO Long-Exposure Image Denoising.” In 2017 IEEE International Conference on Computer Vision (ICCV), 4827–4835. New York, NY, USA: IEEE. doi:10.1109/iccv.2017.516.
- Vancouver
- 1.WANG G, Lopez-Molina C, De Baets B. Blob reconstruction using unilateral second order Gaussian kernels with application to high-ISO long-exposure image denoising. In: 2017 IEEE international conference on computer vision (ICCV). New York, NY, USA: IEEE; 2017. p. 4827–35.
- IEEE
- [1]G. WANG, C. Lopez-Molina, and B. De Baets, “Blob reconstruction using unilateral second order Gaussian kernels with application to high-ISO long-exposure image denoising,” in 2017 IEEE international conference on computer vision (ICCV), Venice, Italy, 2017, pp. 4827–4835.
@inproceedings{8634309,
abstract = {{Blob detection and image denoising are fundamental, and sometimes related, tasks in computer vision. In this paper, we propose a blob reconstruction method using scale-invariant normalized unilateral second order Gaussian kernels. Unlike other blob detection methods, our method suppresses non-blob structures while also identifying blob parameters, i.e., position, prominence and scale, thereby facilitating blob reconstruction. We present an algorithm for high-ISO long-exposure noise removal that results from the combination of our blob reconstruction method and state-of-the-art denoising methods, i.e., the non-local means algorithm (NLM) and the color version of block-matching and 3-D filtering (CBM3D). Experiments on standard images corrupted by real high-ISO long-exposure noise and real-world noisy images demonstrate that our schemes incorporating the blob reduction procedure outperform both the original NLM and CBM3D.}},
author = {{WANG, Gang and Lopez-Molina, Carlos and De Baets, Bernard}},
booktitle = {{2017 IEEE international conference on computer vision (ICCV)}},
isbn = {{9781538610329}},
issn = {{1550-5499}},
keywords = {{GENERALIZED LAPLACIAN,SCALE SELECTION,ALGORITHMS,TRACKING,FILTERS,NUCLEI,NOISE}},
language = {{eng}},
location = {{Venice, Italy}},
pages = {{4827--4835}},
publisher = {{IEEE}},
title = {{Blob reconstruction using unilateral second order Gaussian kernels with application to high-ISO long-exposure image denoising}},
url = {{http://doi.org/10.1109/iccv.2017.516}},
year = {{2017}},
}
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