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.

我们提出了一种新的加法图像因子分解技术，通过在分解过程中调制稀疏性来估计由多个潜在的镜面成分组成的图像。我们的基于模型驱动的 RSFNet 通过将优化展开为网络层来估计这些因子，只需要学习少量的标量。由设计可解释的结果因子可以通过网络进行不同的图像增强任务融合，也可以由用户以可控的方式直接组合。基于 RSFNet，我们详细介绍了一个无需成对或非成对监督训练的零参考低光照增强（LLE）应用。我们的系统在标准基准测试上改善了最先进的性能，并在多个其他数据集上实现了更好的泛化性能。我们还将我们的因子与其他任务特定的融合网络集成，用于去雨、去模糊和去雾等应用，几乎没有额外开销，从而彰显了我们提出的 RSFNet 在多领域和多任务上的泛化能力。项目主页上发布了代码和数据，以便复现。