Fast and reliable structure-oriented video noise estimation

Amer, A.; Dubois, Éric

doi:10.1109/tcsvt.2004.837017

Cited by 171 publications

(42 citation statements)

References 9 publications

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“…As the first step towards segmentation, a background subtraction (as described in 2.1) can be applied, here affecting only this step of the analysis. Second, default de-noising is performed by applying a Gaussian filter with a radius of 0.6 px [59] and by subtracting the estimated mean of the uniform Gaussian white noise, replacing the homogeneity analyzer proposed in [60,61], which is utilized to identify the empty portions of the image, by the Absolute Difference Mask (ADM) edge detector [62]. Candidate object locations are then detected using the ''à trous'' wavelet transform [30], followed by a filtering step in which only single-pixel local maxima detections are kept.…”

Section: Image Segmentationmentioning

confidence: 99%

CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings

Blanchoud

Nicolas

Zoller

et al. 2015

Methods

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Section: Image Segmentationmentioning

confidence: 99%

CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings

Blanchoud

Nicolas

Zoller

et al. 2015

Methods

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“…The X ray image noise is multiplicative. This noise is esti mated using the method reported in [11,12].…”

Section: Use Of Motion Estimations In X Ray Angiographic Imaging Usinmentioning

confidence: 99%

Estimation of Motion in Angiographic Images

Afanasenko

2011

Biomed Eng

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Many types of X ray diagnostic examinations require a series of X ray images. Relative positions of organs and tis sues of the patient in such a series can change from image to image. There are several reasons for such motions:-physiological motions; -involuntary motion of the patient during examina tion;-target oriented changes in X ray apparatus position. Dynamics of internal organs have a negative effect on the quality of X ray images, causing their aberration. The quantitative parameters of such aberrations are variable. For instance, the instantaneous velocity of blood propaga tion varies from 0.5 mm/sec in capillaries to 500 mm/sec in arteries. The velocity of heart tissue motion is 100 mm/sec [1]. Although the amplitude of involuntary patient motion is low, it has a negative effect on the quality of an X ray image. For subtraction angiography, patient motion causes artifacts in vascular system images. In some cases the X ray apparatus is subjected to deliberate changes in position rel ative to the patient during examination. Position sensors integrated into the X ray apparatus provide exact informa tion about such changes in relative position of the patient. Image Processing Specificity in AngiographyAngiographic examination is based on injection of a contrasting agent into the vascular bed. A series of X ray images detects the propagation of the contrasting agent along the vascular bed. Only a fraction of the X ray image is diagnostically significant. This is the image of contrast ing agent filling the blood vessel. The other objects in the X ray image are structural interference. Such objects should be removed from the X ray image using a special mask (X ray image taken before the contrasting agent has been injected). According to the Lambert−Beer law, equations for the current and mask X ray images are:(1)where I and M are current and mask subtraction intensi ties, respectively; I 0 is incident radiation intensity; μ is linear extinction coefficient; x is object thickness; Δμ is change in the extinction coefficient induced by contrast ing agent. The logarithm of the equations (1) and sub traction of mask give Δμ accurate to a constant multipli er:The mask (2) is subtracted using a similar procedure. Patient motion during X ray examination induces differential artifact into the X ray image (Fig. 1b).The subtraction procedure is not always necessary during X ray angiographic image processing. In many cases, the tissues surrounding blood vessel do not intro duce imaging artifact.In addition to mask subtraction, processing of an angiographic X ray image also includes gamma correcThe effect of organ and tissue motion on X ray image quality in angiography is discussed. It is shown that esti mation of such motion may benefit the diagnostic value of images in several ways. The block matching method is described. This method provides information on motion in the form of an offset vector field. The modifications of the block matching algorithm described in this work include static region segmentation based on noi...

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“…Fortunately, the latest noise estimation algorithms provide high accuracy. [7][8][9] Note that the equivalent noise estimation should be applied to both learning and inference phases to prevent a mismatch, as in Fig. 2.…”

Section: Learning Phase 211 Dictionary Classification Accordingmentioning

confidence: 99%

Noise-robust superresolution based on a classified dictionary

Jeong

Song

2010

J. Electron. Imaging

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Conventional learning-based superresolution algorithms tend to boost noise components existing in input images because the algorithms are usually learned in a noise-free environment. Even though a specific noise reduction algorithm is applied to noisy images prior to superresolution, visual quality degradation is inevitable due to the mismatch between noise-free images and denoised images. Accordingly, we present a noise-robust superresolution algorithm that overcomes this problem. In the learning phase, a dictionary classified according to noise level is constructed, and then a high-resolution image is synthesized using the dictionary in the inference phase. Experimental results show that the proposed algorithm outperforms existing algorithms for various noisy images.

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Fast and reliable structure-oriented video noise estimation

Cited by 171 publications

References 9 publications

CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings

CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings

Estimation of Motion in Angiographic Images

Noise-robust superresolution based on a classified dictionary

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