Preserving the Edges of a Digital Image Using Various Filtering Algorithms and Tools

Navjeet, Kumar; Punetha, Deepak; Ehiagwina, Frederick

doi:10.14257/ijsip.2016.9.12.02

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Structural Similarity Index Methods (Ssim)1

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2023

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“…where, l is the luminance (used to compare the brightness between two images), c is the contrast (used to differ the ranges between the brightest and darkest region of two images) and s is the structure (used to compare the local luminance pattern between two images to find the similarity and dissimilarity of the images) and α, β and γ are positive constants [13]. This system calculates the Structural Similarity Index between 2 given images which is a value between -1 and +1.…”

Section: Structural Similarity Index Methods (Ssim)mentioning

confidence: 99%

Image Restoration: A Comparative Analysis of Image De noising Using Different Spatial Filtering Techniques

Onyedinma,

Onyenwe

2023

IJLTEMAS

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Acquired images for medical and other purposes can be affected by noise from both the equipment used in the capturing or the environment. This can have adverse effect on the information therein. Thus, the need to restore the image to its original state by removing the noise. To effectively remove such noise, pre knowledge of the type of noise model present is necessary. This work explores different noise removal filters by first introducing noise to an image and then applying different spatial domain filtering techniques to the image to get rid of the noise. Different evaluation techniques such as Peak to Signal Noise Ratio(PSNR) and Root Mean Square Error(RMSE) were adopted to determine how effective each filter is on a given image noise. Result showed that some filters are more effective on some noise models than others.

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Section: Structural Similarity Index Methods (Ssim)mentioning

confidence: 99%