2007
DOI: 10.1109/acssc.2007.4487266
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Abstract: Abstract= We briefly describe and compare some recent advances in image denoising. In particular, we discuss three leading denoising algorithms, and describe their similarities and differences in terms of both structure and performance. Following a summary of each of these methods, several examples with various images corrupted with simulated and real noise of different strengths are presented. With the help of these experiments, we are able to identify the strengths and weaknesses of these state of the art me… Show more

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Cited by 11 publications
(5 citation statements)
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“…Wavelet coefficient modelling approaches are further subdivided into deterministic and statistical modelling of wavelet coefficients. Accordingly, interesting reviews can be found in Buccigrossi and Simoncelli (1999), Romberg et al (2001), Buades et al (2005), Seo et al (2007).…”
Section: Wavelet Domain Filtersmentioning
confidence: 97%
“…Wavelet coefficient modelling approaches are further subdivided into deterministic and statistical modelling of wavelet coefficients. Accordingly, interesting reviews can be found in Buccigrossi and Simoncelli (1999), Romberg et al (2001), Buades et al (2005), Seo et al (2007).…”
Section: Wavelet Domain Filtersmentioning
confidence: 97%
“…In Figure 12 we compare the proposed algorithm with state of the art denoising algorithms [64] on a noisy Lena image. We present comparisions with Non-local Means filter [65] and Iterative steering kernel regression [66] using standard parameter settings.…”
Section: Resultsmentioning
confidence: 99%
“…Designed to process single images, nonparametric kernel-based filters such as Data-adaptive Kernel Regression and Optimal Spatial Adaption are a general solution to grain removal, but tend to soften some characteristics of CG renders such as high-frequency texture detail and irregular noise patterns [Seo et al 2007]. However, if motion reference is available, a temporal filter will produce better results on any slow moving sequence by sampling across multiple frames, with the results only diminishing under extreme motion or dynamic lighting.…”
Section: Overviewmentioning
confidence: 99%