When taking photos in dim-light environments, due to the small amount of
light entering, the shot images are usually extremely dark, with a great deal
of noise, and the color cannot reflect real-world color. Under this condition,
the traditional methods used for single image denoising have always failed to
be effective. One common idea is to take multiple frames of the same scene to
enhance the signal-to-noise ratio. This paper proposes a recurrent fully
convolutional network (RFCN) to process burst photos taken under extremely
low-light conditions, and to obtain denoised images with improved brightness.
Our model maps raw burst images directly to sRGB outputs, either to produce a
best image or to generate a multi-frame denoised image sequence. This process
has proven to be capable of accomplishing the low-level task of denoising, as
well as the high-level task of color correction and enhancement, all of which
is end-to-end processing through our network. Our method has achieved better
results than state-of-the-art methods. In addition, we have applied the model
trained by one type of camera without fine-tuning on photos captured by
different cameras and have obtained similar end-to-end enhancements.

本文提出了一种基于递归全卷积网络（RFCN）的方法，处理在极低光条件下拍摄的连拍照片，通过端到端处理实现降噪、色彩校正和增强，取得了优于现有方法的效果。

使用循环完全卷积网络进行端到端暗爆图像去噪

End-to-End Denoising of Dark Burst Images Using Recurrent Fully  Convolutional Networks

Different to semantic segmentation, instance segmentation assigns unique
labels to each individual instance of the same class. In this work, we propose
a novel recurrent fully convolutional network architecture for tracking such
instance segmentations over time. The network architecture incorporates
convolutional gated recurrent units (ConvGRU) into a stacked hourglass network
to utilize temporal video information. Furthermore, we train the network with a
novel embedding loss based on cosine similarities, such that the network
predicts unique embeddings for every instance throughout videos. Afterwards,
these embeddings are clustered among subsequent video frames to create the
final tracked instance segmentations. We evaluate the recurrent hourglass
network by segmenting left ventricles in MR videos of the heart, where it
outperforms a network that does not incorporate video information. Furthermore,
we show applicability of the cosine embedding loss for segmenting leaf
instances on still images of plants. Finally, we evaluate the framework for
instance segmentation and tracking on six datasets of the ISBI celltracking
challenge, where it shows state-of-the-art performance.

本文提出三个创新，首先采用循环全卷积网络跟踪每个实例鉴别标签，其次使用余弦相似度嵌入损失函数，为视频中的每个实例预测唯一嵌入，最终将这些嵌入聚类来创建最终跟踪的实例分割。最后，对 ISBI celltracking 挑战的六个数据集进行评估，表现出最先进的性能。