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.

翻译：我们提出一种新的加性图像分解技术，该方法将图像视为由多个潜在镜面反射分量组成，通过调节分解过程中的稀疏性，即可递归地估计这些分量。所提出的模型驱动方法RSFNet通过将优化过程展开为网络层来估计这些因子，仅需学习少量标量参数。这些因子具有天然的可解释性设计，既可通过网络融合用于不同图像增强任务，也可由用户直接以可控方式进行组合。基于RSFNet，我们详细阐述了一个无需配对或非配对监督的零参考低光照增强（LLE）应用。本系统在标准基准测试中提升了当前最优性能，并在多个其他数据集上实现了更优的泛化能力。我们还将这些因子与其它任务特定的融合网络集成，用于去雨、去模糊和去雾等应用，仅增加极小的计算开销，从而凸显了所提出的RSFNet在多领域和多任务中的泛化能力。为便于结果复现，相关代码与数据已在项目主页开源。