Abstract
We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition. Our model-driven {\em RSFNet} estimates these factors by unrolling the optimization into network layers requiring only a few scalars to be learned. The resultant factors are interpretable by design and can be fused for different image enhancement tasks via a network or combined directly by the user in a controllable fashion. Based on RSFNet, we detail a zero-reference Low Light Enhancement (LLE) application trained without paired or unpaired supervision. Our system improves the state-of-the-art performance on standard benchmarks and achieves better generalization on multiple other datasets. We also integrate our factors with other task specific fusion networks for applications like deraining, deblurring and dehazing with negligible overhead thereby highlighting the multi-domain and multi-task generalizability of our proposed RSFNet. The code and data is released for reproducibility on the project homepage.
Abstract (translated)
我们提出了一种新的附加图像因素分解技术，该技术处理由多个潜在极化子组件组成的图像。这些因素可以通过在分解过程中对稀疏度的调节来简单地递归估计。我们的模型驱动的{\em RSFNet}通过将优化展开到仅需要学习几个标量来处理的网络层中来估计这些因素。由此产生的因素可以通过网络或通过用户在可控制的方式进行融合，用于不同的图像增强任务。基于RSFNet，我们详细介绍了一个无需配对或非配对监督的零参考低光增强（LLE）应用。我们的系统在标准基准上提高了最先进的性能，并在多个其他数据集上取得了更好的泛化能力。我们还将我们的因素与其他任务特定的融合网络集成，用于诸如去雾、去噪和去雾等应用。通过显著的 overhead，提高了我们提出的RSFNet的多领域和多任务通用性。代码和数据发布在项目主页上以进行可重复性。
URL
https://arxiv.org/abs/2404.01998
