A Real-Time Technique for Spatio–Temporal Video Noise Estimation

Ghazal, Mohammed; Amer, A.; Ghrayeb, Ali

doi:10.1109/tcsvt.2007.903805

Cited by 35 publications

(9 citation statements)

References 20 publications

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“…The first is to smooth v test and define any difference between v test and its smoothed version to be noise [97,98]. The second is to identify smooth areas in v test and assume that any variation within those smooth areas is noise [99][100][101][102].…”

Section: Direct Estimation Of Mean-squared Errormentioning

confidence: 99%

No-reference image and video quality estimation: Applications and human-motivated design

Hemami

Reibman

2010

Signal Processing: Image Communication

122

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Section: Direct Estimation Of Mean-squared Errormentioning

confidence: 99%

No-reference image and video quality estimation: Applications and human-motivated design

Hemami

Reibman

2010

Signal Processing: Image Communication

122

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“…The results of the proposed algorithm were compared with those of four other algorithms representative of block-based [6], hybrid (filter-and block-based) [8], SVD [11], and PCA algorithms [14], respectively. Thirty test images were used, comprising homogenous images, complex textured images, and MC-rendered images.…”

Section: Resultsmentioning

confidence: 99%

Estimation of Noise Level in Complex Textured Images and Monte Carlo-Rendered Images

2016

KSII TIIS

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The several noise level estimation algorithms that have been developed for use in image processing and computer graphics generally exhibit good performance. However, there are certain special types of noisy images that such algorithms are not suitable for. It is particularly still a challenge to use the algorithms to estimate the noise levels of complex textured photographic images because of the inhomogeneity of the original scenes. Similarly, it is difficult to apply most conventional noise level estimation algorithms to images rendered by the Monte Carlo (MC) method owing to the spatial variation of the noise in such images. This paper proposes a novel noise level estimation method based on histogram modification, and which can be used for more accurate estimation of the noise levels in both complex textured images and MC-rendered images. The proposed method has good performance, is simple to implement, and can be efficiently used in various image-based and graphic applications ranging from smartphone camera noise removal to game background rendition.

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“…the edges and textures) is not present, so that the noise is clearly separated from the useful content. Most of the existing noise estimation methods, like [24,25,26,27,28] estimate a global noise variance in the whole image, while the noise in TOF images is highly non-stationary (see Fig. 2…”

Section: The Proposed Noise Estimation Methods For Tof Depth Imagesmentioning

confidence: 99%

Fuzzy logic-based approach to wavelet denoising of 3D images produced by time-of-flight cameras

Jovanov¹,

Pižurica²,

Philips³

2010

Opt. Express

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In this paper we present a new denoising method for the depth images of a 3D imaging sensor, based on the time-of-flight principle. We propose novel ways to use luminance-like information produced by a time-of flight camera along with depth images. Firstly, we propose a wavelet-based method for estimating the noise level in depth images, using luminance information. The underlying idea is that luminance carries information about the power of the optical signal reflected from the scene and is hence related to the signal-to-noise ratio for every pixel within the depth image. In this way, we can efficiently solve the difficult problem of estimating the non-stationary noise within the depth images. Secondly, we use luminance information to better restore object boundaries masked with noise in the depth images. Information from luminance images is introduced into the estimation formula through the use of fuzzy membership functions. In particular, we take the correlation between the measured depth and luminance into account, and the fact that edges (object boundaries) present in the depth image are likely to occur in the luminance image as well. The results on real 3D images show a significant improvement over the state-of-the-art in the field.

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A Real-Time Technique for Spatio–Temporal Video Noise Estimation

Cited by 35 publications

References 20 publications

No-reference image and video quality estimation: Applications and human-motivated design

No-reference image and video quality estimation: Applications and human-motivated design

Estimation of Noise Level in Complex Textured Images and Monte Carlo-Rendered Images

Fuzzy logic-based approach to wavelet denoising of 3D images produced by time-of-flight cameras

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