Novel methods for Implementation of Efficient Median Filter

Venkatappareddy, P.; Lall, Brejesh; K, Jayanth C; Dinesh, Kamala Kannan; Deepthi, M.

doi:10.1109/indicon.2017.8487508

Cited by 3 publications

(1 citation statement)

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“…Furthermore, in the field of image restoration, Jaiswal and Rajesh ( 2009) and Khmag et al (2015) discussed about the generation of denoising filters based on secondgeneration wavelet transformation. Venkatappareddy et al (2017), proposed two methods based on threshold decomposition and stacking properties for generate median filter.…”

Section: Introductionmentioning

confidence: 99%

Image convolution: a linear programming approach for filters design

et al. 2021

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Image analysis is a branch of signal analysis that focuses on the extraction of meaningful information from images through digital image processing techniques. Convolution is a technique used to enhance specific characteristics of an image, while deconvolution is its inverse process. In this work, we focus on the deconvolution process, defining a new approach to retrieve filters applied in the convolution phase. Given an image I and a filtered image $$I' = f(I)$$ I ′ = f ( I ) , we propose three mathematical formulations that, starting from I and $$I'$$ I ′ , are able to identify the filter $$f'$$ f ′ that minimizes the mean absolute error between $$I'$$ I ′ and $$f'(I)$$ f ′ ( I ) . Several tests were performed to investigate the applicability of our approaches in different scenarios. The results highlight that the proposed algorithms are able to identify the filter used in the convolution phase in several cases. Alternatively, the developed approaches can be used to verify whether a specific input image I can be transformed into a sample image $$I'$$ I ′ through a convolution filter while returning the desired filter as output.

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Section: Introductionmentioning

confidence: 99%