Image Denoising Methods. A New Nonlocal Principle

Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

“…However, many improvements on the original nonlocal means denoising filter have been proposed, some of which could be applied to segmentation. The interested reader can find a review of these improvements in (Buades et al, 2010). For instance, a locally adaptive size of the search volume according to the estimator variance, as suggested by Kervrann and Boulanger (2008), could avoid useless computation in large constant areas (e.g., CSF in ventricle segmentation).…”

Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal

“…We briefly discuss related work on impulse noise removal and low-rank matrix recovery; see [2] and [12] for recent, comprehensive reviews on denoising.…”

“…Given the excellent performance of non-local methods [2,5], learned sparse models [8,18], and the combination of both [6,17] for random Gaussian noise, they were explored for impulse noise as well [20,22]. Non-local methods use redundant visual information within an image (i.e., self-similarity) to group similar image patches together, followed by collaborative filtering [2,5].…”

“…Non-local methods use redundant visual information within an image (i.e., self-similarity) to group similar image patches together, followed by collaborative filtering [2,5]. Sparse methods also use redundant information by assuming each patch can be well approximated by a linear combination of a small subset of patches ('words') within a large dictionary.…”

Adaptive Non-Local Means Denoising Algorithm Using Down-Scaled Images

“…비국부 평균 (Non-local means) 기법은 노이즈 제 거에 매우 효과적인 방식임이 입증되었으며 성능 향 상을 위한 연구가 활발히 진행되고 있다 [1] . 특히 비국 부 평균 필터의 통계적 분석 [4] , 가중치 함수의 선정 [5] , 노이즈 양 및 국부 활동성에 따른 패치 크기의 적응적 결정 [6] , 연산량의 절감을 위한 알고리즘의 가속화 [7] 등과 관련된 연구가 활발히 진행되고 있다.…”