Seed Optimization with Frozen Generator for Superior Zero-shot Low-light
  Enhancement
Yuxuan Gu Yi Jin Ben Wang Zhixiang Wei Xiaoxiao Ma Pengyang Ling Haoxuan Wang Huaian Chen Enhong Chen
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.