Abstract
Real-world low-light images captured by imaging devices suffer from poor visibility and require a domain-specific enhancement to produce artifact-free outputs that reveal details. In this paper, we propose an unpaired low-light image enhancement network leveraging novel controlled transformation-based self-supervision and unpaired self-conditioning strategies. The model determines the required degrees of enhancement at the input image pixels, which are learned from the unpaired low-lit and well-lit images without any direct supervision. The self-supervision is based on a controlled transformation of the input image and subsequent maintenance of its enhancement in spite of the transformation. The self-conditioning performs training of the model on unpaired images such that it does not enhance an already-enhanced image or a well-lit input image. The inherent noise in the input low-light images is handled by employing low gradient magnitude suppression in a detail-preserving manner. In addition, our noise handling is self-conditioned by preventing the denoising of noise-free well-lit images. The training based on low-light image enhancement-specific attributes allows our model to avoid paired supervision without compromising significantly in performance. While our proposed self-supervision aids consistent enhancement, our novel self-conditioning facilitates adequate enhancement. Extensive experiments on multiple standard datasets demonstrate that our model, in general, outperforms the state-of-the-art both quantitatively and subjectively. Ablation studies show the effectiveness of our self-supervision and self-conditioning strategies, and the related loss functions.
Abstract (translated)
现实世界中，成像设备捕获的低光图像由于可见度差而需要特定领域的增强处理，以生成无伪影且揭示细节的输出。本文提出了一种利用新颖的基于受控变换的自监督和未配对自我条件策略的低光图像增强网络。该模型根据从未经配对的昏暗图像与良好照明图像中学习到的信息来确定输入图像像素所需的增强程度，而无需任何直接监督。这种自监督基于输入图像的受控转换，并随后在转换后保持其增强效果。自我条件化策略则通过训练模型以不增强已经过增强或光照良好的输入图像的方式，在未配对图像上进行训练。 输入低光图像中的固有噪声被采用细节保留方式的低梯度幅度抑制来处理，同时我们的噪声处理是自适应的，可以防止去噪无噪声的良好照明图像。基于低光图像增强特定属性的训练使得我们的模型能够在不显著降低性能的情况下避免配对监督。虽然我们提出的自监督有助于持续一致地进行增强，但新颖的自我条件化策略则促进了适当的增强。 在多个标准数据集上的广泛实验表明，总体而言，我们的模型无论是定量还是定性评估都优于现有技术。消融研究表明了我们自监督和自我条件化策略以及相关损失函数的有效性。
URL
https://arxiv.org/abs/2503.00642
