Single-Image Noise Level Estimation for Blind Denoising

Liu, Xinhao; Tanaka, Masayuki; Okutomi, Masatoshi

doi:10.1109/tip.2013.2283400

Cited by 385 publications

(210 citation statements)

References 19 publications

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“…For each image we estimate the noise by using the algorithm described in ref. 33. While the signal strength increases with the patch size, the estimated noise levels in all five measurements are identical (within 1.5% of the noise mean).…”

Section: Recovered Geometrymentioning

confidence: 61%

Locating and classifying fluorescent tags behind turbid layers using time-resolved inversion

et al. 2015

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The use of fluorescent probes and the recovery of their lifetimes allow for significant advances in many imaging systems, in particular, medical imaging systems. Here we propose and experimentally demonstrate reconstructing the locations and lifetimes of fluorescent markers hidden behind a turbid layer. This opens the door to various applications for non-invasive diagnosis, analysis, flowmetry and inspection. The method is based on a time-resolved measurement that captures information about both fluorescence lifetime and spatial position of the probes. To reconstruct the scene, the method relies on a sparse optimization framework to invert time-resolved measurements. This wide-angle technique does not rely on coherence, and does not require the probes to be directly in line of sight of the camera, making it potentially suitable for long-range imaging.

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Section: Recovered Geometrymentioning

confidence: 61%

Locating and classifying fluorescent tags behind turbid layers using time-resolved inversion

et al. 2015

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“…Filter-based approaches [4][5], in which noise images are processed by high-pass or low-pass filters using a convolution. The noise level is calculated on the basis of the filtered image for high-pass filters or the difference between the original and filtered images for low-pass filters.…”

Section: Introductionmentioning

confidence: 99%

“…Patch-based approaches or block-based methods [4], in which the image is divided into homogeneous blocks. Within the blocks, the noise level is calculated, for example, by the method of principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

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Automatic Highly Accurate Estimation of Gaussian Noise Level in Digital Images Using Filtration and Edges Detection Methods

Balovsyak¹,

Odaiska²

2017

IJIGSP

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“…This includes, but not limited to, the content based image retrieval (CBIR) [1], biomedical imaging [2], GIS [3], photography [4] etc. The image acquisition in these applications is mainly based on raster imaging which is very prone to the surrounding noise and system distortion [5]. In the literature researchers have devised classical filters for image de-noising.…”

Section: Introductionmentioning

confidence: 99%

A Novel Two Dimensional Adaptive Filtering Algorithm for Image De-Noising via Fractional Gradient

Rizvi¹,

Ahmad²,

Zubair³

et al. 2017

IJSEIA

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In the recent decade it has been witnessed that raster images are the primary source of information for numerous applications such as bio-medical, law enforcement, geographical information system (GIS), photography, astronomy, etc. Primarily, the quality of raster images compromises due to the surrounding factors of these applications. Because, it is very difficult to control surrounding parameter (light, motion, distance) while acquiring images. Therefore, the image acquisition in these applications is very much prone to the noise. In the literature, researchers have targeted this issue and have already devised classical image filters for image de-noising. Afterwards, in the recent years the performance of classical filtering was further improved by employing two dimensional adaptive filters (2-DAF) for image de-noising and enhancement. In the literature, researchers have reported the performance comparisons of various 2-DAF specifically for image restoration, enhancement, estimation, and de-noising. In this paper an extended version of one dimensional fractional least mean square (1-DFLMS) to two dimensional fractional least mean square (2-DFLMS) is presented. Moreover the performance of the proposed algorithm has been rigorously compared with the existing and most employed 2-DAF algorithm namely, two dimensional least mean square (2-DLMS), two dimensional variable step size least mean square (2-DVSSLMS). The simulation results illustrate the notable performance edge of the proposed algorithm with the existing approaches.Keywords: Image de-noising, two-dimensional adaptive filtering, least mean square (LMS), variable step size least mean square (VSSLMS), fractional least mean square (FLMS)

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Single-Image Noise Level Estimation for Blind Denoising

Cited by 385 publications

References 19 publications

Locating and classifying fluorescent tags behind turbid layers using time-resolved inversion

Locating and classifying fluorescent tags behind turbid layers using time-resolved inversion

Automatic Highly Accurate Estimation of Gaussian Noise Level in Digital Images Using Filtration and Edges Detection Methods

A Novel Two Dimensional Adaptive Filtering Algorithm for Image De-Noising via Fractional Gradient

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