A Digital Noise Reducer for Encoded NTSC Signals

McMann, Renville H.; Kreinik, Stephen; Moore, J. K.; Kaiser, Andreas; Rossi, John

doi:10.5594/j17406

Cited by 31 publications

(5 citation statements)

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“…When motion is present, it is possible to select the filter parameters as a compromise between the amount of blurring and the degree of noise reduction. In broadcast television images, the speed of motion varies continually both with time and with position, and, consequently, nonlinear temporal filters have been designed which automatically select the best filter parameters depending on the amount of motion present (McMann et al, 1978;Rossi, 1978;Dennis, 1980). In electron microscopy, however, manual selection of filter parameters is usually adequate.…”

Section: Introductionmentioning

confidence: 99%

Reduction of noise in TV rate electron microscope images by digital filtering

Erasmus

1982

Journal of Microscopy

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Section: Introductionmentioning

confidence: 99%

Reduction of noise in TV rate electron microscope images by digital filtering

Erasmus

1982

Journal of Microscopy

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“…In [31], Dubois and Sabri applied this concept to noise reduction in television signals. Their work was further extended to include the use of recursive filters [32,33].…”

Section: B Dirt Detection With Motion Compensationmentioning

confidence: 99%

Segmentation-Assisted Detection of Dirt Impairments in Archived Film Sequences

Ren

Vlachos

2007

IEEE Trans. Syst., Man, Cybern. B

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In this correspondence, a novel segmentation-assisted method for film-dirt detection is proposed. We exploit the fact that film dirt manifests in the spatial domain as a cluster of connected pixels whose intensity differs substantially from that of its neighborhood, and we employ a segmentation-based approach to identify this type of structure. A key feature of our approach is the computation of a measure of confidence attached to detected dirt regions, which can be utilized for performance fine tuning. Another important feature of our algorithm is the avoidance of the computational complexity associated with motion estimation. Our experimental framework benefits from the availability of manually derived as well as objective ground-truth data obtained using infrared scanning. Our results demonstrate that the proposed method compares favorably with standard spatial, temporal, and multistage median-filtering approaches and provides efficient and robust detection for a wide variety of test materials.

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“…Notice that this weighting function is slightly different to the one used in [24] for which . We choose the warping between the lines and such that (8) In this way, we may define the denoised image by the formula (4), that is, we put in each position of the denoised line the average of all the matches of on the lines of . We illustrate this procedure in Fig.…”

Section: A Warping Through Stereo Matchingmentioning

confidence: 99%

“…They are based on averaging pixels of different frames trying to avoid the blurring effect where motion occurs, they are the temporal counterpart of edge preserving spatial filters in that temporal edges are related to motion. Examples include different types of adaptive median filters and order statistic filters [5], [6] or recursive filters [7], [8] (see [4]). Motion compensated filters are based on the assumption that the variation of the pixel gray level over a motion trajectory is mainly due to noise, and, thus, averaging these values should give a good estimate of the true pixel value; they produce high-quality results.…”

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confidence: 99%

Movie Denoising by Average of Warped Lines

Bertalmío¹,

Caselles²,

Pardo³

2007

IEEE Trans. on Image Process.

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Here, we present an efficient method for movie denoising that does not require any motion estimation. The method is based on the well-known fact that averaging several realizations of a random variable reduces the variance. For each pixel to be denoised, we look for close similar samples along the level surface passing through it. With these similar samples, we estimate the denoised pixel. The method to find close similar samples is done via warping lines in spatiotemporal neighborhoods. For that end, we present an algorithm based on a method for epipolar line matching in stereo pairs which has per-line complexity O (N), where N is the number of columns in the image. In this way, when applied to the image sequence, our algorithm is computationally efficient, having a complexity of the order of the total number of pixels. Furthermore, we show that the presented method is unsupervised and is adapted to denoise image sequences with an additive white noise while respecting the visual details on the movie frames. We have also experimented with other types of noise with satisfactory results.

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A Digital Noise Reducer for Encoded NTSC Signals

Cited by 31 publications

References 0 publications

Reduction of noise in TV rate electron microscope images by digital filtering

Reduction of noise in TV rate electron microscope images by digital filtering

Segmentation-Assisted Detection of Dirt Impairments in Archived Film Sequences

Movie Denoising by Average of Warped Lines

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