Wang Gang, Lopez-Molina Carlos, De Baets Bernard
IEEE Trans Image Process. 2020 Apr 14. doi: 10.1109/TIP.2020.2986687.
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.
斑点检测和图像去噪是计算机视觉中的基础任务，有时二者相互关联。在本文中，我们提出一种计算方法，使用归一化单侧二阶高斯核来定量测量斑点特征。该方法在抑制非斑点结构的同时，还能对斑点的位置、突出程度和尺度进行定量测量，这有助于斑点重建和斑点缩减任务。随后，我们提出一种去噪方案，用于处理高ISO长曝光噪声，这种噪声有时在空间上呈现出斑点外观，我们采用斑点缩减程序作为传统去噪方法的一种低成本预处理。我们将所提出的去噪方法应用于真实世界的噪声图像以及被真实噪声破坏的标准图像。实验结果证明了所提方法相对于现有最先进去噪方法的优越性。
