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)(2017)
摘要
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.
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关键词
high-ISO long-exposure noise removal,high-ISO long-exposure image denoising method,computer vision,nonblob structure suppression,NLM method,CBM3D,color block-matching-3D filtering version,blob detection methods,scale-invariant normalized unilateral second order Gaussian kernels,blob reduction procedure,real-world noisy images,nonlocal means algorithm,blob reconstruction method
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