Consensus-based 3D view generation from noisy images

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Noise significantly degrades the image quality of 3D views synthesized by convolutional neural networks like neX.
Experimental results showed improvements of up to 1.300 dB in Peak Signal Noise Ratio and 0.032 in Structural Similarity Index Measure.
The study implements a new strategy calculating consensus among networks to enhance quality from noisy images.
Finding consensus among networks improves the synthesized image quality, especially with noisy inputs during testing.

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This analysis reveals image quality improvements in 3D views generated by neX under noise conditions, suggesting effective consensus methods.

Rodríguez-Rodríguez et al. (2025) studied this question.

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This analysis reveals image quality improvements in 3D views generated by neX under noise conditions, suggesting effective consensus methods.