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Multi-Exposure Image Fusion using Convolutional Neural Network (CNN) | IEEE Conference Publication | IEEE Xplore

Multi-Exposure Image Fusion using Convolutional Neural Network (CNN)


Abstract:

Because calculating the amount of light in a photograph has been the topic of so much research, multiexposure photos frequently have a smeared appearance. The fundamental...Show More

Abstract:

Because calculating the amount of light in a photograph has been the topic of so much research, multiexposure photos frequently have a smeared appearance. The fundamental purpose of these investigations is to identify the patch-smart solution for illumination estimation with human intervention using the CNN method, which can be utilized to distinguish between distinct time periods in multi-exposure fusion photos. Common picture fusion segmentation techniques, such as the seeded watershed algorithm, are time-consuming and error-prone. In addition, parameter adjustment is necessary when employing such algorithms, therefore a competent consumer is desirable. This research intends to address the critical issues connected with patch-wise illumination estimation in multi-source photo fusion. Using convolutional neural networks, multiple-publicity image fusion snapshots will be created. The neural network can be trained with visual data that has been manually tagged, and its performance can be measured with summative metrics.
Date of Conference: 20-22 October 2022
Date Added to IEEE Xplore: 14 November 2022
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Conference Location: Ankara, Turkey

References

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