We present an effective and efficient approach for low-light image enhancement, named Lookup Table Global Curve Estimation (LUT-GCE). In contrast to existing curve-based methods with pixel-wise adjustment, we propose to estimate a global curve for the entire image that allows corrections for both under- and over-exposure. Specifically, we develop a novel cubic curve formulation for light enhancement, which enables an image-adaptive and pixel-independent curve for the range adjustment of an image. We then propose a global curve estimation network (GCENet), a very light network with only 25.4k parameters. To further speed up the inference speed, a lookup table method is employed for fast retrieval. In addition, a novel histogram smoothness loss is designed to enable zero-shot learning, which is able to improve the contrast of the image and recover clearer details. Quantitative and qualitative results demonstrate the effectiveness of the proposed approach. Furthermore, our approach outperforms the state of the art in terms of inference speed, especially on high-definition images (e.g., 1080p and 4k).

翻译：我们提出了一种高效的低光图像增强方法，名为基于查找表的全局曲线估计（LUT-GCE）。与现有基于像素级调整的曲线方法不同，我们提出为整幅图像估计一条全局曲线，该曲线能够同时校正欠曝光和过曝光区域。具体而言，我们开发了一种新颖的立方曲线公式用于亮度增强，该公式能够实现图像自适应且与像素无关的曲线，从而调整图像的整体范围。随后，我们提出了一个全局曲线估计网络（GCENet），该网络极其轻量，仅包含25.4k个参数。为进一步提升推理速度，我们采用查找表方法进行快速检索。此外，我们设计了新颖的直方图平滑损失函数以支持零样本学习，该损失函数能够增强图像对比度并恢复更清晰的细节。定量和定性结果证明了所提方法的有效性。此外，我们的方法在推理速度上优于现有最先进技术，尤其是在高清图像（如1080p和4k）上表现更为突出。