Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Image Enhancement

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, for the low-light image enhancement process of a single image, we introduce the pre-trained generators to restore the details and colors degraded by low-light conditions, thereby improving the visual effect. 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 image 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 image enhancement model can significantly regularize the visibility 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 effectiveness of the proposed method, showing its potential advantages over numerous state-of-the-art methods both qualitatively and quantitatively.