Image processing with texture feature preservation by three-state locally adaptive filter

Tsymbal, Oleg V.; Ponomarenko, Nikolay N.; Егиазарян, Карен; Astola, Jaakko

doi:10.1117/12.511518

Cited by 6 publications

(8 citation statements)

References 16 publications

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“…It performed quite well, but later it has been shown that the local statistic Lee filter was not the best choice [15]. DCT-based filter performs considerably better [15]. This will be confirmed in Section 3.…”

Section: Introductionsupporting

confidence: 60%

“…To name a few, we mention the papers of Yunhan et al [10], Aiazzi et al [12], as well as the book of Perry et al [13] (see also references therein). Preliminary results of the current paper have been published in [14,15]. The analysis here is more thorough and extended in many respects, one of the most important of which is additionally studied additive noise case.…”

Section: Introductionmentioning

confidence: 92%

“…The situation where an image is corrupted by the former type of noise is considered in this paper in more detail compared to [15].…”

Section: Image/noise Modelsmentioning

confidence: 99%

“…The local statistic Lee filter [16] was proposed in [14] for application in texture regions. It performed quite well, but later it has been shown that the local statistic Lee filter was not the best choice [15]. DCT-based filter performs considerably better [15].…”

Section: Introductionmentioning

confidence: 97%

“…These were the reasons for our decision to modify the hard switching two-state locally adaptive filter to the threestate one [14,15]. The proposed filter had the states corresponding to the three classes: image homogeneous regions, edges and details, and texture regions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing

Tsymbal¹,

Ponomarenko

Zelensky

et al. 2005

EURASIP J. Adv. Signal Process.

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Textural features are one of the most important types of useful information contained in images. In practice, these features are commonly masked by noise. Relatively little attention has been paid to texture preserving properties of noise attenuation methods. This stimulates solving the following tasks: (1) to analyze the texture preservation properties of various filters; and (2) to design image processing methods capable to preserve texture features well and to effectively reduce noise. This paper deals with examining texture feature preserving properties of different filters. The study is performed for a set of texture samples and different noise variances. The locally adaptive three-state schemes are proposed for which texture is considered as a particular class. For "detection" of texture regions, several classifiers are proposed and analyzed. As shown, an appropriate trade-off of the designed filter properties is provided. This is demonstrated quantitatively for artificial test images and is confirmed visually for real-life images.

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Section: Introductionsupporting

confidence: 60%