Deep learning-based low-light image enhancement (LLIE) is a task of leveraging deep neural networks to enhance the image illumination while keeping the image content unchanged. From the perspective of training data, existing methods complete the LLIE task driven by one of the following three data types: paired data, unpaired data and zero-reference data. Each type of these data-driven methods has its own advantages, e.g., zero-reference data-based methods have very low requirements on training data and can meet the human needs in many scenarios. In this paper, we leverage pure Gaussian noise to complete the LLIE task, which further reduces the requirements for training data in LLIE tasks and can be used as another alternative in practical use. Specifically, we propose Noise SElf-Regression (NoiSER) without access to any task-related data, simply learns a convolutional neural network equipped with an instance-normalization layer by taking a random noise image, $\mathcal{N}(0,\sigma^2)$ for each pixel, as both input and output for each training pair, and then the low-light image is fed to the trained network for predicting the normal-light image. Technically, an intuitive explanation for its effectiveness is as follows: 1) the self-regression reconstructs the contrast between adjacent pixels of the input image, 2) the instance-normalization layer may naturally remediate the overall magnitude/lighting of the input image, and 3) the $\mathcal{N}(0,\sigma^2)$ assumption for each pixel enforces the output image to follow the well-known gray-world hypothesis when the image size is big enough. Compared to current state-of-the-art LLIE methods with access to different task-related data, NoiSER is highly competitive in enhancement quality, yet with a much smaller model size, and much lower training and inference cost. Besides, NoiSER also excels in mitigating overexposure and handling joint tasks.

翻译：基于深度学习的低光照图像增强（LLIE）是一项利用深度神经网络提升图像照度同时保持图像内容不变的任务。从训练数据的角度来看，现有方法通过以下三种数据类型之一驱动完成LLIE任务：配对数据、非配对数据以及零参考数据。这些数据驱动方法各有优势，例如，基于零参考数据的方法对训练数据要求极低，能在许多场景下满足人类需求。本文利用纯高斯噪声完成LLIE任务，这进一步降低了LLIE任务对训练数据的要求，可作为实际应用中的另一种替代方案。具体而言，我们提出噪声自回归（NoiSER）方法，该方法无需任何任务相关数据，仅通过将随机噪声图像（每个像素服从$\mathcal{N}(0,\sigma^2)$分布）同时作为每个训练对的输入和输出来学习一个配备实例归一化层的卷积神经网络，随后将低光照图像输入训练好的网络以预测正常光照图像。从技术层面看，其有效性的直观解释如下：1）自回归过程重建了输入图像相邻像素间的对比度；2）实例归一化层可自然地校正输入图像的整体幅度/光照；3）当图像尺寸足够大时，每个像素的$\mathcal{N}(0,\sigma^2)$假设强制输出图像遵循著名的灰度世界假说。与当前需要不同任务相关数据的先进LLIE方法相比，NoiSER在增强质量上具有高度竞争力，同时模型尺寸更小，训练与推理成本显著降低。此外，NoiSER在抑制过曝光和处理联合任务方面也表现优异。