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

<p>Automatic bokeh is one of the smartphone’s essential photography effects. This effect enhances the quality of the image where the subject background gets out of focus by providing a soft (i.e., diverse) background. Most smartphones have a single rear camera that is lacking to provide which effects need to be applied to which kind of images. To do the same, smartphones depend on different software to generate the bokeh effect on images. Blur, Color-point, Zoom, Spin, Big Bokeh, Color Picker, Low-key, High-Key, and Silhouette are the popular bokeh effects. With this wide range of bokeh types available, it is difficult for the user to choose a suitable effect for their images. Deep Learning (DL) models (i.e., MobileNetV2, InceptionV3, and VGG16) are used in this work to recommend high-quality bokeh effects for images. Four thousand five hundred images are collected from online resources such as Google images, Unsplash, and Kaggle to examine the model performance. 85% accuracy has been achieved for recommending different bokeh effects using the proposed model MobileNetV2, which exceeds many of the existing models.</p>
</abstract>

Automatic bokeh is one of the smartphone’s essential photography effects. This effect enhances the quality of the image where the subject background gets out of focus by providing a soft (i.e., diverse) background. Most smartphones have a single rear camera that is lacking to provide which effects need to be applied to which kind of images. To do the same, smartphones depend on different software to generate the bokeh effect on images. Blur, Color-point, Zoom, Spin, Big Bokeh, Color Picker, Low-key, High-Key, and Silhouette are the popular bokeh effects. With this wide range of bokeh types available, it is difficult for the user to choose a suitable effect for their images. Deep Learning (DL) models (i.e., MobileNetV2, InceptionV3, and VGG16) are used in this work to recommend high-quality bokeh effects for images. Four thousand five hundred images are collected from online resources such as Google images, Unsplash, and Kaggle to examine the model performance. 85% accuracy has been achieved for recommending different bokeh effects using the proposed model MobileNetV2, which exceeds many of the existing models.

Rendering automatic bokeh recommendation engine for photos using deep learning algorithm

Acta Universitatis Sapientiae, Informatica

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

Automatic bokeh is one of the smartphone’s essential photography effects. This effect enhances the quality of the image where the subject background gets out...

Rendering automatic bokeh recommendation engine for photos using deep learning algorithm

Publié en ligne: 04 févr. 2023

Pages: 248 - 272

Reçu: 09 nov. 2022

Accepté: 17 déc. 2022

DOI: https://doi.org/10.2478/ausi-2022-0015

Mots clés
bokeh, recommendation, photography, deep learning, inceptionV3, VGG16, mobileNetV2, effects

© 2022 Rakesh Kumar et al., published by Sciendo

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

Rendering automatic bokeh recommendation engine for photos using deep learning algorithm

Publié en ligne: 04 févr. 2023

Pages: 248 - 272

Reçu: 09 nov. 2022

Accepté: 17 déc. 2022

DOI: https://doi.org/10.2478/ausi-2022-0015

Mots clésbokeh, recommendation, photography, deep learning, inceptionV3, VGG16, mobileNetV2, effects

© 2022 Rakesh Kumar et al., published by Sciendo

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

Mots clés
bokeh, recommendation, photography, deep learning, inceptionV3, VGG16, mobileNetV2, effects