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Blob reconstruction using unilateral second order Gaussian kernels with application to high-ISO long-exposure image denoising

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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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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.
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. In 2017 IEEE international conference on computer vision (ICCV) (pp. 4827–4835). New York, NY, USA: IEEE.
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.
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.
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://dx.doi.org/10.1109/iccv.2017.516},
  year         = {2017},
}

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