High-Iso Long-Exposure Image Denoising Based On Quantitative Blob Characterization
IEEE TRANSACTIONS ON IMAGE PROCESSING(2020)
摘要
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.
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关键词
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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