Kozłowski Wojciech, Szachniewicz Michał, Stypułkowski Michał, Zięba Maciej
Faculty of Information and Communication Technology, Wrocław University of Science and Technology, 50-370 Wrocław, Poland.
Faculty of Mathematics and Computer Science, University of Wrocław, 50-384 Wrocław, Poland.
Entropy (Basel). 2024 Aug 26;26(9):726. doi: 10.3390/e26090726.
Enhancing low-light images with natural colors poses a challenge due to camera processing variations and limited access to ground-truth lighting conditions. To address this, we propose Dimma, a semi-supervised approach that aligns with any camera using a small set of image pairs captured under extreme lighting conditions. Our method employs a convolutional mixture density network to replicate camera-specific noise present in dark images. We enhance results further by introducing a conditional UNet architecture based on user-provided lightness values. Trained on just a few real image pairs, Dimma achieves competitive results compared to fully supervised state-of-the-art methods trained on large datasets.
由于相机处理的差异以及难以获取真实的光照条件，增强具有自然色彩的低光照图像面临着挑战。为了解决这个问题，我们提出了Dimma，这是一种半监督方法，它使用在极端光照条件下拍摄的一小组图像对来与任何相机对齐。我们的方法采用卷积混合密度网络来复制暗图像中存在的特定于相机的噪声。我们通过引入基于用户提供的亮度值的条件UNet架构进一步提升结果。Dimma仅在少数真实图像对上进行训练，与在大型数据集上训练的全监督的先进方法相比，取得了具有竞争力的结果。
