@article{8660585,
  abstract     = {{Blob detection and image denoising are fundamental, sometimes related tasks in computer vision. In this paper, we present a computational method to quantitatively measure blob characteristics using normalized unilateral second-order Gaussian kernels. This method suppresses non-blob structures while yielding a quantitative measurement of the position, prominence and scale of blobs, which can facilitate the tasks of blob reconstruction and blob reduction. Subsequently, we propose a denoising scheme to address high-ISO long-exposure noise, which sometimes spatially shows a blob appearance, employing a blob reduction procedure as a cheap preprocessing for conventional denoising methods. We apply the proposed denoising methods to real-world noisy images as well as standard images that are corrupted by real noise. The experimental results demonstrate the superiority of the proposed methods over state-of-the-art denoising methods.}},
  author       = {{WANG, Gang and Lopez-Molina, Carlos and De Baets, Bernard}},
  issn         = {{1057-7149}},
  journal      = {{IEEE TRANSACTIONS ON IMAGE PROCESSING}},
  keywords     = {{Software,Computer Graphics and Computer-Aided Design,Noise reduction,Kernel,Blob detection,Image denoising,Task analysis,Image reconstruction,Noise measurement,Image denoising,real-world noise,high-ISO long-exposure images,blob detection,blob characterization,second-order Gaussian kernel,GENERALIZED LAPLACIAN,NOISE-REDUCTION,EDGE-DETECTION,DECOMPOSITION}},
  language     = {{eng}},
  pages        = {{5993--6005}},
  title        = {{High-ISO long-exposure image denoising based on quantitative blob characterization}},
  url          = {{http://dx.doi.org/10.1109/tip.2020.2986687}},
  volume       = {{29}},
  year         = {{2020}},
}

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