Abstract
Recent advancements in image translation for enhancing mixed-exposure images have demonstrated the transformative potential of deep learning algorithms. However, addressing extreme exposure variations in images remains a significant challenge due to the inherent complexity and contrast inconsistencies across regions. Current methods often struggle to adapt effectively to these variations, resulting in suboptimal performance. In this work, we propose HipyrNet, a novel approach that integrates a HyperNetwork within a Laplacian Pyramid-based framework to tackle the challenges of mixed-exposure image enhancement. The inclusion of a HyperNetwork allows the model to adapt to these exposure variations. HyperNetworks dynamically generates weights for another network, allowing dynamic changes during deployment. In our model, the HyperNetwork employed is used to predict optimal kernels for Feature Pyramid decomposition, which enables a tailored and adaptive decomposition process for each input image. Our enhanced translational network incorporates multiscale decomposition and reconstruction, leveraging dynamic kernel prediction to capture and manipulate features across varying scales. Extensive experiments demonstrate that HipyrNet outperforms existing methods, particularly in scenarios with extreme exposure variations, achieving superior results in both qualitative and quantitative evaluations. Our approach sets a new benchmark for mixed-exposure image enhancement, paving the way for future research in adaptive image translation.
Abstract (translated)
最近在图像翻译领域，特别是在增强混合曝光图片方面的进展已经展示了深度学习算法的变革潜力。然而，处理极端曝光差异仍然是一个重大挑战，因为不同区域之间的固有复杂性和对比度不一致使这一问题更加棘手。目前的方法往往难以有效适应这些变化，导致性能不佳。 为此，我们提出了HipyrNet，这是一种创新方法，它在拉普拉斯金字塔（Laplacian Pyramid）框架内集成了超网络（HyperNetwork），用于解决混合曝光图像增强的挑战。通过引入超网络，模型能够更好地适应不同的曝光差异。超网络可以动态生成另一个网络的权重，在部署时实现动态调整。 在我们的模型中，所使用的超网络被用来预测特征金字塔分解的最佳核函数，这使每个输入图片都能获得量身定制且具有适应性的分解过程。我们改进的翻译网络结合了多尺度分解与重建技术，并利用动态核函数预测来捕捉和操控不同尺度上的特性。 广泛的实验表明，在处理极端曝光差异的情况下，HipyrNet的表现优于现有的方法，并在定性和定量评估中均取得了卓越的结果。我们的方法为混合曝光图像增强设定了新的基准，并为进一步研究适应性图像翻译铺平了道路。
URL
https://arxiv.org/abs/2501.05195
