NeRF-MIR: Towards High-Quality Restoration of Masked Images with Neural Radiance Fields

• URL: http://arxiv.org/abs/2601.17350v1
• Date: Sat, 24 Jan 2026 07:32:06 GMT
• Title: NeRF-MIR: Towards High-Quality Restoration of Masked Images with Neural Radiance Fields
• Authors: Xianliang Huang, Zhizhou Zhong, Shuhang Chen, Yi Xu, Juhong Guan, Shuigeng Zhou,
• Abstract summary: This paper introduces NeRF-MIR, a novel neural rendering approach specifically proposed for the restoration of masked images.<n>We introduce a textbfProgressively textbfIterative textbfREstoration (textbfPIRE) mechanism to restore the masked regions.<n>Experiments on real data and constructed datasets demonstrate the superiority of NeRF-MIR over its counterparts in masked image restoration.
• Score: 23.86601141998747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Radiance Fields (NeRF) have demonstrated remarkable performance in novel view synthesis. However, there is much improvement room on restoring 3D scenes based on NeRF from corrupted images, which are common in natural scene captures and can significantly impact the effectiveness of NeRF. This paper introduces NeRF-MIR, a novel neural rendering approach specifically proposed for the restoration of masked images, demonstrating the potential of NeRF in this domain. Recognizing that randomly emitting rays to pixels in NeRF may not effectively learn intricate image textures, we propose a \textbf{P}atch-based \textbf{E}ntropy for \textbf{R}ay \textbf{E}mitting (\textbf{PERE}) strategy to distribute emitted rays properly. This enables NeRF-MIR to fuse comprehensive information from images of different views. Additionally, we introduce a \textbf{P}rogressively \textbf{I}terative \textbf{RE}storation (\textbf{PIRE}) mechanism to restore the masked regions in a self-training process. Furthermore, we design a dynamically-weighted loss function that automatically recalibrates the loss weights for masked regions. As existing datasets do not support NeRF-based masked image restoration, we construct three masked datasets to simulate corrupted scenarios. Extensive experiments on real data and constructed datasets demonstrate the superiority of NeRF-MIR over its counterparts in masked image restoration.

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