<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In many visual perception studies, external visual noise is used as a methodology to broaden the understanding of information processing of visual stimuli. The underlying assumption is that two sources of noise limit sensory processing: the external noise inherent in the environmental signals and the internal noise or internal variability at different levels of the neural system. Usually, when external noise is added to an image, it is evenly distributed. However, the color intensity and image contrast are modified in this way, and it is unclear whether the visual system responds to their change or the noise presence. We aimed to develop several methods of noise generation with different distributions that keep the global image characteristics. These methods are appropriate in various applications for evaluating the internal noise in the visual system and its ability to filter the added noise. As these methods destroy the correlation in image intensity of neighboring pixels, they could be used to evaluate the role of local spatial structure in image processing.</p>
</abstract>

In many visual perception studies, external visual noise is used as a methodology to broaden the understanding of information processing of visual stimuli. The underlying assumption is that two sources of noise limit sensory processing: the external noise inherent in the environmental signals and the internal noise or internal variability at different levels of the neural system. Usually, when external noise is added to an image, it is evenly distributed. However, the color intensity and image contrast are modified in this way, and it is unclear whether the visual system responds to their change or the noise presence. We aimed to develop several methods of noise generation with different distributions that keep the global image characteristics. These methods are appropriate in various applications for evaluating the internal noise in the visual system and its ability to filter the added noise. As these methods destroy the correlation in image intensity of neighboring pixels, they could be used to evaluate the role of local spatial structure in image processing.

Noise Generation Methods Preserving Image Color Intensity Distributions

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Noise Generation Methods Preserving Image Color Intensity Distributions"}

In many visual perception studies, external visual noise is used as a methodology to broaden the understanding of information processing of visual stimuli. The...

Noise Generation Methods Preserving Image Color Intensity Distributions

Publié en ligne: 22 sept. 2022

Pages: 111 - 126

Reçu: 04 mai 2022

Accepté: 06 juin 2022

DOI: https://doi.org/10.2478/cait-2022-0031

Mots clés
Visual Noise, Gaussian Noise, Image quality, image power spectrum

© 2022 Tsvetalin Totev et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Noise Generation Methods Preserving Image Color Intensity Distributions

Publié en ligne: 22 sept. 2022

Pages: 111 - 126

Reçu: 04 mai 2022

Accepté: 06 juin 2022

DOI: https://doi.org/10.2478/cait-2022-0031

Mots clésVisual Noise, Gaussian Noise, Image quality, image power spectrum

© 2022 Tsvetalin Totev et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Visual Noise, Gaussian Noise, Image quality, image power spectrum