This paper presents a novel network structure with illumination-aware gamma
correction and complete image modelling to solve the low-light image
enhancement problem. Low-light environments usually lead to less informative
large-scale dark areas, directly learning deep representations from low-light
images is insensitive to recovering normal illumination. We propose to
integrate the effectiveness of gamma correction with the strong modelling
capacities of deep networks, which enables the correction factor gamma to be
learned in a coarse to elaborate manner via adaptively perceiving the deviated
illumination. Because exponential operation introduces high computational
complexity, we propose to use Taylor Series to approximate gamma correction,
accelerating the training and inference speed. Dark areas usually occupy large
scales in low-light images, common local modelling structures, e.g., CNN,
SwinIR, are thus insufficient to recover accurate illumination across whole
low-light images. We propose a novel Transformer block to completely simulate
the dependencies of all pixels across images via a local-to-global hierarchical
attention mechanism, so that dark areas could be inferred by borrowing the
information from far informative regions in a highly effective manner.
Extensive experiments on several benchmark datasets demonstrate that our
approach outperforms state-of-the-art methods.

本文提出了一种新颖的网络结构，利用感知偏差照明方式和完整图像建模来解决低光照图像增强问题。通过将伽马校正的有效性与深度网络的强建模能力相结合，我们能够粗到细地自适应地感知偏离的照明来学习校正因子伽马。通过使用泰勒级数近似伽马校正，加速了训练和推理速度。我们提出了一种新颖的变压器块，通过本地到全局的层次化注意机制完全模拟跨图像像素的依赖关系，从而以高度有效的方式从具有很好信息的区域推断出暗区。在几个基准数据集上的广泛实验表明，我们的方法优于现有方法。

基于光照感知伽马校正和完整图像建模网络的低光照图像增强

Low-Light Image Enhancement with Illumination-Aware Gamma Correction and  Complete Image Modelling Network

Low light very likely leads to the degradation of an image's quality and even
causes visual task failures. Existing image enhancement technologies are prone
to overenhancement, color distortion or time consumption, and their
adaptability is fairly limited. Therefore, we propose a new single low-light
image lightness enhancement method. First, an energy model is presented based
on the analysis of membrane vibrations induced by photon stimulations. Then,
based on the unique mathematical properties of the energy model and combined
with the gamma correction model, a new global lightness enhancement model is
proposed. Furthermore, a special relationship between image lightness and gamma
intensity is found. Finally, a local fusion strategy, including segmentation,
filtering and fusion, is proposed to optimize the local details of the global
lightness enhancement images. Experimental results show that the proposed
algorithm is superior to nine state-of-the-art methods in avoiding color
distortion, restoring the textures of dark areas, reproducing natural colors
and reducing time cost. The image source and code will be released at
this https URL

该论文提出了一种基于光子激发引起的膜振动分析的能量模型，并结合伽马校正模型提出了一种新的全局亮度增强模型，最终还加入了一种局部融合策略，该算法在避免色彩失真、恢复暗区纹理、还原自然色彩和减少时间成本方面优于九种先进方法。

利用细胞振动模型进行低光图像增强

Low-light Image Enhancement Using the Cell Vibration Model

The goal of computational color constancy is to preserve the perceptive
colors of objects under different lighting conditions by removing the effect of
color casts caused by the scene's illumination. With the rapid development of
deep learning based techniques, significant progress has been made in image
semantic segmentation. In this work, we exploit the semantic information
together with the color and spatial information of the input image in order to
remove color casts. We train a convolutional neural network (CNN) model that
learns to estimate the illuminant color and gamma correction parameters based
on the semantic information of the given image. Experimental results show that
feeding the CNN with the semantic information leads to a significant
improvement in the results by reducing the error by more than 40%.

本文利用图像语义分割的信息，结合输入图像的颜色和空间信息，训练了一个 CNN 模型来消除场景照明造成的色偏，试验结果表明，利用 CNN 模型的图像语义分割信息可以将误差减少超过 40%。