Application of rough sets for edge enhancing image filters

Wójcik, Zbigniew M.

doi:10.1109/icip.1994.413626

Cited by 12 publications

(4 citation statements)

References 6 publications

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“…Wojcik used rough sets derived from an equivalence relation that could ''take noise X apart from image I'' to do edge enhancement (Wojcik, 1994). The problem here is that finding such an equivalence relation seems to obviate the need for any further processing to do the edge detection.…”

Section: Rough Sets On Image Spacesmentioning

confidence: 98%

A rough set-based approach to handling spatial uncertainty in binary images

Sinha¹,

Laplante²

2004

Engineering Applications of Artificial Intelligence

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Section: Rough Sets On Image Spacesmentioning

confidence: 98%

A rough set-based approach to handling spatial uncertainty in binary images

Sinha¹,

Laplante²

2004

Engineering Applications of Artificial Intelligence

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“…(3) According to the condition attributes of rough sets theory the original image is classified into three class image; (4) The different filtering method is adopted to process the different equivalence class pixels (based on the neighborhood gray of original image); (5) Three subimages are merged into an new SAR image which the speckle has been filtered; (6) Proof-testing the effects of image with filtering speckle.…”

Section: Intelligent Filtering Steps Based On Rough Sets Theorymentioning

confidence: 99%

Method for speckle intelligent filtering of SAR images based on rough sets theory

Wang

Yang

2001

SPIE Proceedings

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“…Image filtering is a very general concept, including artistic filters, curves, tone adjustment (Lischinski et al, 2006;Farbman et al, 2008), color transfer (Reinhard et al, 2001;Xiang et al, 2009;Cusano et al, 2012;Wu et al, 2013), image enhancement (Son et al, 2014;Wojcik, 1994), and so forth. It is the key element in many graphics algorithms as well as an important tool in many image editing applications.…”

Section: Introductionmentioning

confidence: 99%

Laplace operator based multi-channel image filters learning

Liu

et al. 2016

JAMDSM

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Learning an image filter from a pair of images composed of an original image and its filtered version has attracted increasing attention in the image processing region. However, the existing methods only take the color information into consideration to learn an image filter, which cannot accurately depict the luminance and some style changes of images so that the generated learning results are inaccurate. This paper proposes a Laplace operator based multichannel learning approach to learn a multi-channel filter from an original image and its filtered version. Different from previous method, we adopt a strategy to learn a multi-channel filter which can accurately reconstruct the filtered image. In addition, benefiting from the Laplace regularization based multi-channel learning model, we can make the neighbor pixels of each channel have the similar weights and learn more homogeneous weight maps for each channel filter, thereby generating more accurate results with less time. Due to the multi-channel representation of our learnt filter, we can edit the luminance style and the color style of images by changing the weights of different channel filter. Furthermore, we can edit the effect of the image filters by tuning the weights of some basis filters. At last, extensive experiments well validate the performance of our method over state-of-the-art methods in terms of accuracy and speed.

show abstract

Application of rough sets for edge enhancing image filters

Cited by 12 publications

References 6 publications

A rough set-based approach to handling spatial uncertainty in binary images

A rough set-based approach to handling spatial uncertainty in binary images

Method for speckle intelligent filtering of SAR images based on rough sets theory

Laplace operator based multi-channel image filters learning

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