A new cascaded spatio-temporal noise reduction scheme for interlaced video

Jostschulte, K.; Amer, A.

doi:10.1109/icip.1998.723439

Cited by 8 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Using low-pass filtering, spatially uncorrelated noise can be reduced while preserving the original signal if the frame is unstructured. Structured frames with fine details like edges and corners gets blurred with such filtering [5].…”

Section: Proposed Approachmentioning

confidence: 99%

“…The output of applying these operators to the pixels with close intensity values is close to 0. A kernel size of W = 3 was used in [5]. For highly noisy images, however, using a kernel of W = 5 achieves theoretically higher gain than with W = 3.…”

Section: Proposed Approachmentioning

confidence: 99%

“…The detection of image structure is done using eight intensity-homogeneity analyzers as in [5] (Fig. 1).…”

Section: Proposed Approachmentioning

confidence: 99%

“…In this paper, we improve upon the technique proposed in [5] with 1) increased gain by using a noiseadaptive kernel size and using the two most homogeneous directions and 2) increased preservation of image content by applying the 2σ η criterion directionally.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed approach is based on [5] where an adaptive averaging Homogeneity-based Filter (HF) is applied to the intensity most homogeneous direction. The authors in [5] introduced an effective criteria to measure homogeneity.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Homogeneity-based directional sigma filtering of video noise

Ghazal

Amer

Ghrayeb

2005

IEEE International Conference on Image Processing 2005

Self Cite

View full text Add to dashboard Cite

This paper proposes a real-time method for the reduction of white Gaussian video noise. The method achieves a maximum gain of 4.8 dB and is capable of preserving image content. It adapts window size, weighting and behavior to both image content and noise level in order to optimize the filtering. It starts by detecting the intensity-homogeneous direction from 8 different candidates. A variant of the Sigma filter is then applied directionally. The filtering is performed along homogeneous areas and not across edges. For noisy images, the filtering is increased automatically by using the two most homogeneous directions with a larger kernel size. The proposed filter achieves better image preservation by turning off gradually for less noisy images. It works well for both highly noisy and good-quality images without the introduction of speed or hardware implementation challenges.

show abstract

Section: Proposed Approachmentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

“…The detection of image structure is done using eight intensity-homogeneity analyzers as in [5] (Fig. 1).…”

Section: Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%