An efficient approach for removal of impulse noise from highly corrupted images by preserving edge details

Pompapathi, M.; Krishna, A. Sri; Babu, B. Raveendra

doi:10.1109/icsip.2010.5697526

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The above generic expression can be improved in order to obtain various values for impulses of the same magnitude located in various backdrops [21]. The revised expression of this filter will be as follows:…”

Section: The Corruption Estimation Filtermentioning

confidence: 99%

Spatial domain noise removal filtering for low-resolution digital images

Salah,

Al-Sit,

Salah

et al. 2024

IJEECS

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<p>n this research work, six different filters are applied on a low resolution 8 b/pixel gray-scale images, which operate on small sub-images (windows of 3×3 to 11×11 pixels). The enhanced images are used to compare the efficiency of the different six filters using the peak signal to noise ratio (PSNR) image quality measure. Noise peak elimination filter (PSNR)=36.63) outperforms others, such as median filter (PSNR=36.61), while corruption estimation (PSNR=36.03) significantly cuts processing time by only processing the corrupted pixels while maintaining image details. Mean filter (PSNR=34.05) is sensitive to outliers, which cause the image's sharpness and fine features to be lost. By avoiding averaging across edges, bimodal-averaging filter (PSNR=35.30), which improves on the mean filter, chooses the mean of the biggest population. The median-mean filtering (PSNR=36.32), which combines median and mean filters and determines the output pixel by averaging the median and some nearby pixels, is another improvement above averaging.</p>

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Section: The Corruption Estimation Filtermentioning

confidence: 99%