A fast novel algorithm for salt and pepper impulse noise removal using B-Splines for finger print forensic images

Jayasree, P.; Kumar, Pradeep

doi:10.1109/iciip.2013.6707628

Cited by 2 publications

(1 citation statement)

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“…Indeed, median filtering is regarded as the standard approach to deal with impulsive noise. More sophisticated methods such as Neural Networks combined with fuzzy logic [8], noise removal using B-Splines [10] and use of directional filters [31] were also proposed to tackle the same problem. In this study, a Genetic Algorithm (GA) for optimizing a multi-stage non-flat soft morphological filter is presented.…”

Section: Introductionmentioning

confidence: 99%

Soft morphological filter optimization using a genetic algorithm for noise elimination

Ercal

Özcan

Asta

2014

2014 14th UK Workshop on Computational Intelligence (UKCI)

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Abstract-Digital image quality is of importance in almost all image processing applications. Many different approaches have been proposed for restoring the image quality depending on the nature of the degradation. One of the most common problems that cause such degradation is impulse noise. In general, well known median filters are preferred for eliminating different types of noise. Soft morphological filters are recently introduced and have been in use for many purposes. In this study, we present a Genetic Algorithm (GA) which combines different objectives as a weighted sum under a single evaluation function and generates a soft morphological filter to deal with impulse noise, after a training process with small images. The automatically generated filter performs better than the median filter and achieves comparable results to the best known filters from the literature over a set of benchmark instances that are larger than the training instances. Moreover, although the training process involves only impulse noise added images, the same evolved filter performs better than the median filter for eliminating Gaussian noise as well.

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Section: Introductionmentioning

confidence: 99%