Neural radiance field has achieved fundamental success in novel view
synthesis from input views with the same brightness level captured under fixed
normal lighting. Unfortunately, synthesizing novel views remains to be a
challenge for input views with heterogeneous brightness level captured under
low-light condition. The condition is pretty common in the real world. It
causes low-contrast images where details are concealed in the darkness and
camera sensor noise significantly degrades the image quality. To tackle this
problem, we propose to learn to decompose illumination, reflectance, and noise
from input views according to that reflectance remains invariant across
heterogeneous views. To cope with heterogeneous brightness and noise levels
across multi-views, we learn an illumination embedding and optimize a noise map
individually for each view. To allow intuitive editing of the illumination, we
design an illumination adjustment module to enable either brightening or
darkening of the illumination component. Comprehensive experiments demonstrate
that this approach enables effective intrinsic decomposition for low-light
multi-view noisy images and achieves superior visual quality and numerical
performance for synthesizing novel views compared to state-of-the-art methods.

从具有异质亮度级别的低光条件下捕获的输入视图中合成新视图始终是一项挑战。本文提出了学习将亮度、光反射和噪声从输入视图中分解的方法，以便在异质视图中保持光反射不变。通过学习照明嵌入和优化噪声映射，以及设计照明调整模块来实现对照明的直观编辑，该方法实现了低光多视图噪声图像的有效内在分解，并在合成新视图方面在视觉质量和数值性能上优于现有方法。

从异质低光捕获中学习新近景生成

Learning Novel View Synthesis from Heterogeneous Low-light Captures

Images taken under the low-light condition often contain blur and saturated
pixels at the same time. Deblurring images with saturated pixels is quite
challenging. Because of the limited dynamic range, the saturated pixels are
usually clipped in the imaging process and thus cannot be modeled by the linear
blur model. Previous methods use manually designed smooth functions to
approximate the clipping procedure. Their deblurring processes often require
empirically defined parameters, which may not be the optimal choices for
different images. In this paper, we develop a data-driven approach to model the
saturated pixels by a learned latent map. Based on the new model, the non-blind
deblurring task can be formulated into a maximum a posterior (MAP) problem,
which can be effectively solved by iteratively computing the latent map and the
latent image. Specifically, the latent map is computed by learning from a map
estimation network (MEN), and the latent image estimation process is
implemented by a Richardson-Lucy (RL)-based updating scheme. To estimate
high-quality deblurred images without amplified artifacts, we develop a prior
estimation network (PEN) to obtain prior information, which is further
integrated into the RL scheme. Experimental results demonstrate that the
proposed method performs favorably against state-of-the-art algorithms both
quantitatively and qualitatively on synthetic and real-world images.

在本文中，我们开发了一种数据驱动方法来对饱和像素进行建模，并提出了基于学习的潜变量映射进行非盲目去模糊处理的最大后验 (MAP) 问题，可通过迭代计算潜变量映射和潜变量图像来有效解决。为了提高去模糊图像的质量并避免加大伪影，我们开发了先验估计网络 (PEN) 来获取先验信息，并将其融入到 RL 方案中。实验结果表明，所提出的方法在合成图像和真实世界图像上在定量和定性方面都表现出色，优于现有的算法。