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High-ISO Long-Exposure Image Denoising Based on Quantitative Blob Characterization
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DOI：10.1109/TIP.2020.2986687.png)
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Abstract
En 中文
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.
Keywords:
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
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