3-D Median Structures for Image Sequence Filtering and Coding

Viero, T.; Neuvo, Y.

doi:10.1007/978-1-4615-3236-1_14

Cited by 8 publications

(6 citation statements)

References 58 publications

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“…Note that the spatial and temporal separability assumption conveyed through (9) suggests that the general form of a decorrelation matrix is (11) where is the temporal decorrelation (or transform) matrix. The following theorem provides the unique transform matrix which can decorrelate (8) into 2-D matrix-vector equations.…”

Section: Decorrelation Of a Group Of Framesmentioning

confidence: 99%

“…404-405]. The separability assumption on the correlation function is equivalently representable in Kronecker product form (9) where and are, respectively, the temporal and spatial covariance matrices of the original GOF. A motioncompensated GOF is usually more stationary along the temporal direction than spatial ones as described in the previous section.…”

Section: Decorrelation Of a Group Of Framesmentioning

confidence: 99%

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A motion-compensated spatio-temporal filter for image sequences with signal-dependent noise

Boo

Bose²

1998

IEEE Trans. Circuits Syst. Video Technol.

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In this paper, a novel spatio-temporal filter is described for monochrome image sequences with either signalindependent or signal-dependent noise by considering both spatial and temporal correlations. With the assumptions of spatiotemporal separability and temporal stationarity, it is shown that motion-compensated groups of frames can be decorrelated by using the Karhunen-Loeve transform. Practical filters that work well on a variety of image sequences are developed by first applying the Hadamard transform along the temporal direction. Subsequently, the parametric adaptive Wiener filter is applied to each of the resulting approximately decorrelated transformed images. These transformed images are classified into one average image and a remaining set of residual images, which provide interesting and useful interpretations of the type of image sequence. The filter performance is evaluated by considering different types of image sequences in the database. The procedure advanced for processing a sequence of monochrome images can be adapted for generalization to multispectral images and this possibility is currently under detailed investigation.

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Section: Decorrelation Of a Group Of Framesmentioning

confidence: 99%

Section: Decorrelation Of a Group Of Framesmentioning

confidence: 99%

A motion-compensated spatio-temporal filter for image sequences with signal-dependent noise

Boo

Bose²

1998

IEEE Trans. Circuits Syst. Video Technol.

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“…However, the time-varying images or image sequences can be considered as spatiotemporal data [1], that is, a time sequence of two-dimensional (2D) images. The fact that the third dimension, that is, time is included increases computing complexity and time processing.…”

Section: Introductionmentioning

confidence: 99%

“…One-dimensional filters remove noise without impairing the spatial resolution in stationary areas. In the case of large motion, the performance of temporal filters is insufficient [1,2,3] and the temporal filtering must be connected with motion compensation [4] so as to filter objects along their motion trajectory. However, this way is very computationally complex, and because of spatial warping and scene changes, the motion compensation often does not work well.…”

Section: Introductionmentioning

confidence: 99%

“…For that reason, the spatiotemporal filters can produce the best estimate in comparison with temporal and spatial filters. In addition, the spatiotemporal filtering [1,2,3,8,9,10] can excellently remove the noise with the simultaneous signal blurring reduction (including spatial and motion blurring). Many works related to spatiotemporal filtering show that median filters are not suitable for image sequence filtering due to their introducing of signal-details blurring.…”

Section: Introductionmentioning

confidence: 99%

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LUM Smoother with Smooth Control for Noisy Image Sequences

Lukáč

Marchevsky

2001

EURASIP J. Adv. Signal Process.

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This paper focuses on adaptive structure of LUM (lower-upper-middle) smoothers for noisy image sequences. For the balance between noise suppression and signal-detail preservation, the LUM smoothers are widely used in smoothing applications. The amount of smoothing done by LUM smoothers is controlled by tuning parameter. However, the smoothing level is fixed for whole image. Thus, the excessive or insufficient smoothing can be performed. This problem is solved by a new method based on the adaptive controlled level of smoothing. A new method has excellent performance of the noise reduction in the environments corrupted by the impulse noise. In addition, minimal signal-detail and motion blurring can be observed. The performance of proposed method is evaluated through objective criteria and compared with traditional temporal, spatial, and spatiotemporal LUM smoothers.

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