  In this work, we observe that the generators, which are pre-trained on
massive natural images, inherently hold the promising potential for superior
low-light image enhancement against varying scenarios.Specifically, we embed a
pre-trained generator to Retinex model to produce reflectance maps with
enhanced detail and vividness, thereby recovering features degraded by
low-light conditions.Taking one step further, we introduce a novel optimization
strategy, which backpropagates the gradients to the input seeds rather than the
parameters of the low-light enhancement model, thus intactly retaining the
generative knowledge learned from natural images and achieving faster
convergence speed. Benefiting from the pre-trained knowledge and
seed-optimization strategy, the low-light enhancement model can significantly
regularize the realness and fidelity of the enhanced result, thus rapidly
generating high-quality images without training on any low-light dataset.
Extensive experiments on various benchmarks demonstrate the superiority of the
proposed method over numerous state-of-the-art methods qualitatively and
quantitatively.
