- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) - Zhenjiang, China (2019.8.4-2019.8.8)] 2019 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) - Modification of Wettability Property of NITI Alloy by Laser Texturing and Carbon Ion Implantation
摘要： Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial denoised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD-based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.
关键词： self-similarity,Back projection,patch grouping,image denoising,low-rank approximation (LRA),singular value decomposition (SVD)
更新于2025-09-23 15:21:01
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[IEEE 2019 Days on Diffraction (DD) - St. Petersburg, Russia (2019.6.3-2019.6.7)] 2019 Days on Diffraction (DD) - Novel types of mode dispersion of optical vortices in twisted optical fibers
摘要： Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial denoised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD-based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.
关键词： patch grouping,Back projection,low-rank approximation (LRA),singular value decomposition (SVD),image denoising,self-similarity
更新于2025-09-19 17:13:59
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Fast Restoration of Aberration-Degraded Extended Object Based on Local Region Abstraction
摘要： The compensating capability of wavefront sensorless adaptive optics (WFSless AO) system for extended object images degraded by aberration is severely limited to correction speed due to largely sized images and low frame rate of camera. To improve the correction capability of WFSless AO system, a method based on local region abstraction (LRA) is presented according to the sparse characteristics of extended object imaging. The experiment results of image restoration with WFSless AO system based on LRA show that the correction speed of this method is 8–20 times faster than that of the previous method for the static aberration, the image restoration speed is improved by about 3.5 times under the dynamic aberration.
关键词： image restoration,correction speed,Adaptive optics system,LRA,aberration
更新于2025-09-10 09:29:36