Digital Techniques for Reducing Television Noise

Rossi, John

doi:10.5594/j17407

Cited by 19 publications

(17 citation statements)

References 3 publications

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“…Originally, coring was developed as a heuristic technique for crisping television pictures [5]. In the early 1980s, coring was first applied in the digital domain for noise reduction [3], [4], [6]. The technique of coring received a great deal of attention after Donoho and Johnstone applied it successfully in the wavelet transform domain in 1994 [1], [2].…”

mentioning

confidence: 99%

Embedded coring in MPEG video compression

Roosmalen

Lagendijk

Biemond

2002

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Coring is a well-known technique for removing noise from images. The mechanism of coring consists of transforming a noise-degraded image into a frequency-domain representation, followed by a reduction of the image transform coefficients by a (nonlinear) coring function. After inverse transforming the cored coefficients, the noise-reduced image is obtained. In this paper, we show that coring can be embedded into MPEG encoders with relatively little additional complexity. We exploit statistical properties of the DCT coefficients to find the optimal Bayesian coring function for each of the DCT coefficients. Experimental results show the effectiveness of the MPEG-embedded coring function on compressing noisy image sequences.

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confidence: 99%

Embedded coring in MPEG video compression

Roosmalen

Lagendijk

Biemond

2002

IEEE Trans. Circuits Syst. Video Technol.

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“…Early methods include decomposing the image into a set of wavelets. Low-amplitude wavelet values are simply suppressed to remove noise in a method call coring [30], [35]. Other techniques include anisotropic diffusion [26] and bilateral filtering [36].…”

Section: Image Denoisingmentioning

confidence: 99%

Image Restoration by Matching Gradient Distributions

Cho¹,

Zitnick

Joshi

et al. 2012

IEEE Trans. Pattern Anal. Mach. Intell.

123

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Abstract-The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. We present an alternative deconvolution method called iterative distribution reweighting (IDR) which imposes a global constraint on gradients so that a reconstructed image should have a gradient distribution similar to a reference distribution. In natural images, a reference distribution not only varies from one image to another, but also within an image depending on texture. We estimate a reference distribution directly from an input image for each texture segment. Our algorithm is able to restore rich mid-frequency textures. A large-scale user study supports the conclusion that our algorithm improves the visual realism of reconstructed images compared to those of MAP estimators.

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“…The probability of the distortion gives P b (v − u|u) = P (v|u) (v|u) [21][22][23]54]. The non-stationary distribution that models the distortion of the joint intensity statistics is:…”

Section: Appendix a Derivation Of The Psf P B Of The Statistical mentioning

confidence: 99%

“…However, the entropy is optimized for aligned mode distributions and that can be limiting [20]. Thus, the histogram has been combined with the coring methodology [21] that was originally developed for noise removal [22,23]. In the context of MRI inhomogeneity correction, coring has been combined with a spatial smoothness constraint for the field [24,25].…”

Section: Introductionmentioning

confidence: 99%

Restoration of Bi-Contrast MRI Data for Intensity Uniformity with Bayesian Coring of Co-Occurrence Statistics

et al. 2017

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Abstract:The reconstruction of MRI data assumes a uniform radio-frequency field. However, in practice, the radio-frequency field is inhomogeneous and leads to anatomically inconsequential intensity non-uniformities across an image. An anatomic region can be imaged with multiple contrasts reconstructed independently and be suffering from different non-uniformities. These artifacts can complicate the further automated analysis of the images. A method is presented for the joint intensity uniformity restoration of two such images. The effect of the intensity distortion on the auto-co-occurrence statistics of each image as well as on the joint-co-occurrence statistics of the two images is modeled and used for their non-stationary restoration followed by their back-projection to the images. Several constraints that ensure a stable restoration are also imposed. Moreover, the method considers the inevitable differences between the signal regions of the two images. The method has been evaluated extensively with BrainWeb phantom brain data as well as with brain anatomic data from the Human Connectome Project (HCP) and with data of Parkinson's disease patients. The performance of the proposed method has been compared with that of the N4ITK tool. The proposed method increases tissues contrast at least 4.62 times more than the N4ITK tool for the BrainWeb images. The dynamic range with the N4ITK method for the same images is increased by up to +29.77%, whereas, for the proposed method, it has a corresponding limited decrease of −1.15%, as expected. The validation has demonstrated the accuracy and stability of the proposed method and hence its ability to reduce the requirements for additional calibration scans.

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Digital Techniques for Reducing Television Noise

Cited by 19 publications

References 3 publications

Embedded coring in MPEG video compression

Embedded coring in MPEG video compression

Image Restoration by Matching Gradient Distributions

Restoration of Bi-Contrast MRI Data for Intensity Uniformity with Bayesian Coring of Co-Occurrence Statistics

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