Neural Radiance Field (NeRF) has received much attention in recent years due
to the impressively high quality in 3D scene reconstruction and novel view
synthesis. However, image degradation caused by the scattering of atmospheric
light and object light by particles in the atmosphere can significantly
decrease the reconstruction quality when shooting scenes in hazy conditions. To
address this issue, we propose Dehazing-NeRF, a method that can recover clear
NeRF from hazy image inputs. Our method simulates the physical imaging process
of hazy images using an atmospheric scattering model, and jointly learns the
atmospheric scattering model and a clean NeRF model for both image dehazing and
novel view synthesis. Different from previous approaches, Dehazing-NeRF is an
unsupervised method with only hazy images as the input, and also does not rely
on hand-designed dehazing priors. By jointly combining the depth estimated from
the NeRF 3D scene with the atmospheric scattering model, our proposed model
breaks through the ill-posed problem of single-image dehazing while maintaining
geometric consistency. Besides, to alleviate the degradation of image quality
caused by information loss, soft margin consistency regularization, as well as
atmospheric consistency and contrast discriminative loss, are addressed during
the model training process. Extensive experiments demonstrate that our method
outperforms the simple combination of single-image dehazing and NeRF on both
image dehazing and novel view image synthesis.

Dehazing-NeRF 是一种无监督方法，通过联合学习大气散射模型和清晰的 Neural Radiance Field 模型，解决拍摄模糊场景时大气散射对图像重建的影响，同时改善信息丢失造成的图像质量降低，具有更好的图像去雾和新视角合成效果。