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arXiv Computer Vision By Zhengguo Li, Chaobing Zheng

Physics Guided Diffusion Model Based Exposure Interpolation for High Dynamic Range Imaging

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The paper introduces a physics-guided diffusion model for exposure interpolation in high dynamic range imaging, aiming to eliminate brightness order reversal artifacts that arise when fusing two large-exposure-ratio SDR images. The method first interpolates an initial image using a physics-driven component, then refines high-frequency details with a diffusion model that leverages correlation analysis to reduce noise-target variation and fitting error amplification. Experiments demonstrate that this approach outperforms existing convolutional neural network-based exposure interpolation algorithms by generating richer high-frequency content.

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