Night images suffer not only from low light, but also from uneven
distributions of light. Most existing night visibility enhancement methods
focus mainly on enhancing low-light regions. This inevitably leads to over
enhancement and saturation in bright regions, such as those regions affected by
light effects (glare, floodlight, etc). To address this problem, we need to
suppress the light effects in bright regions while, at the same time, boosting
the intensity of dark regions. With this idea in mind, we introduce an
unsupervised method that integrates a layer decomposition network and a
light-effects suppression network. Given a single night image as input, our
decomposition network learns to decompose shading, reflectance and
light-effects layers, guided by unsupervised layer-specific prior losses. Our
light-effects suppression network further suppresses the light effects and, at
the same time, enhances the illumination in dark regions. This light-effects
suppression network exploits the estimated light-effects layer as the guidance
to focus on the light-effects regions. To recover the background details and
reduce hallucination/artefacts, we propose structure and high-frequency
consistency losses. Our quantitative and qualitative evaluations on real images
show that our method outperforms state-of-the-art methods in suppressing night
light effects and boosting the intensity of dark regions.

该论文提出了一种无监督的方法，通过层分解网络和光效抑制网络集成来来压制夜间图像中光效，并增强暗区域的照度

无监督夜间图像增强：层分解与光效抑制的结合

Unsupervised Night Image Enhancement: When Layer Decomposition Meets  Light-Effects Suppression

Neuron pruning is an efficient method to compress the network into a slimmer
one for reducing the computational cost and storage overhead. Most of
state-of-the-art results are obtained in a layer-by-layer optimization mode. It
discards the unimportant input neurons and uses the survived ones to
reconstruct the output neurons approaching to the original ones in a
layer-by-layer manner. However, an unnoticed problem arises that the
information loss is accumulated as layer increases since the survived neurons
still do not encode the entire information as before. A better alternative is
to propagate the entire useful information to reconstruct the pruned layer
instead of directly discarding the less important neurons. To this end, we
propose a novel Layer Decomposition-Recomposition Framework (LDRF) for neuron
pruning, by which each layer's output information is recovered in an embedding
space and then propagated to reconstruct the following pruned layers with
useful information preserved. We mainly conduct our experiments on ILSVRC-12
benchmark with VGG-16 and ResNet-50. What should be emphasized is that our
results before end-to-end fine-tuning are significantly superior owing to the
information-preserving property of our proposed framework.With end-to-end
fine-tuning, we achieve state-of-the-art results of 5.13x and 3x speed-up with
only 0.5% and 0.65% top-5 accuracy drop respectively, which outperform the
existing neuron pruning methods.

本文提出了一种新的神经元修剪框架 —— 分层分解重构框架（LDRF），它能够在嵌入空间中恢复每一层的输出信息，并传播至后续被修剪的层，保留有用信息，相比现有方法在 ILSVRC-12 基准测试中能够实现 5.13-3 倍的速度提升，并且只有 0.5％和 0.65％的 top-5 准确性下降。