Abstract
The choice of image filters in computer vision has a significant effect on the image reconstruction and feature extraction. Currently, the most filters are used to enhance images for human consumptions, programmed operations and to reduce the noise, frequency levels in the image. Though it is hard to select an optimal set of filters for a given series of images, in this work, we propose to choose the best assortment of different filters for a given image as the input. By generating the pixel array of the input image, we compute all the image attributes such as RGB colour mean, variance, mean squared error and signal-to-noise ratio values of the input image and then compare with the same, once the filter is applied. We verify the effectiveness of the filters by conducting an empirical evaluation with best-discovered traits.
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Kurama, V., Sridevi, T. (2020). Image Filter Selection, Denoising and Enhancement Based on Statistical Attributes of Pixel Array. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 766. Springer, Singapore. https://doi.org/10.1007/978-981-13-9683-0_27
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DOI: https://doi.org/10.1007/978-981-13-9683-0_27
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