ZDL: Zero-Shot Degradation Factor Learning for Robust and Efficient Image Enhancement

In recent years, many existing learning-based image enhancement methods have shown excellent performance. However, these methods heavily rely on the labeled training data and are limited by the data distribution and application scenarios. To address these limitations, inspired by Hadamard theory, we propose a Zero-shot Degradation Factor Learning (ZDL) for robust and efficient image enhancement, which also could be extended to various harsh scenarios. Specifically, we first design a degradation factor estimation network based on Hadamard theory, which estimates the degradation factors for images to be enhanced. Then, by introducing controlled model perturbations, we propose a new learning strategy. By synthesizing additional data and exploring the inherent connections between different data, we enhance the image by relying solely on the input image and not requiring any other reference. Extensive quantitative and qualitative experimental results fully demonstrate the superiority of the proposed method, and ablation studies also verify the effectiveness of our carefully designed learning strategy.