Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement
This addresses the problem of enhancing low-light images for computer vision applications, offering a zero-shot approach that is incremental by building on existing pre-trained models.
The paper tackles low-light image enhancement by embedding a pre-trained generator into a Retinex model and optimizing input seeds instead of model parameters, achieving superior results without training on low-light data, as shown by extensive benchmarks.
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.