Abstract
Due to the wide dynamic range in real low-light scenes, there will be large differences in the degree of contrast degradation and detail blurring of captured images, making it difficult for existing end-to-end methods to enhance low-light images to normal exposure. To address the above issue, we decompose low-light image enhancement into a recursive enhancement task and propose a brightness-perceiving-based recursive enhancement framework for high dynamic range low-light image enhancement. Specifically, our recursive enhancement framework consists of two parallel sub-networks: Adaptive Contrast and Texture enhancement network (ACT-Net) and Brightness Perception network (BP-Net). The ACT-Net is proposed to adaptively enhance image contrast and details under the guidance of the brightness adjustment branch and gradient adjustment branch, which are proposed to perceive the degradation degree of contrast and details in low-light images. To adaptively enhance images captured under different brightness levels, BP-Net is proposed to control the recursive enhancement times of ACT-Net by exploring the image brightness distribution properties. Finally, in order to coordinate ACT-Net and BP-Net, we design a novel unsupervised training strategy to facilitate the training procedure. To further validate the effectiveness of the proposed method, we construct a new dataset with a broader brightness distribution by mixing three low-light datasets. Compared with eleven existing representative methods, the proposed method achieves new SOTA performance on six reference and no reference metrics. Specifically, the proposed method improves the PSNR by 0.9 dB compared to the existing SOTA method.
Abstract (translated)
由于真实低光场景中存在的宽动态范围，拍摄到的图像在对比度退化和细节模糊程度上会有很大的差异，这使得现有的端到端方法难以将低光图像增强至正常曝光。为了解决上述问题，我们将低光图像增强任务分解成一个递归增强的任务，并提出了一种基于亮度感知的递归增强框架来改进高动态范围下的低光图像质量。 具体而言，我们的递归增强框架由两个并行的子网络组成：自适应对比度和纹理增强网络（ACT-Net）以及亮度感知网络（BP-Net）。其中，ACT-Net旨在通过亮度调整分支和梯度调整分支的指导来自适应地增强图像的对比度和细节。这两个分支被设计用于感知低光条件下图像中对比度和细节退化的程度。为了根据不同的亮度水平自适应地增强图像，我们提出了BP-Net，它通过探索图像亮度分布特性来控制ACT-Net的递归增强次数。 最后，为了协调ACT-Net和BP-Net的工作，我们设计了一种新颖的无监督训练策略，以简化并促进整个训练过程。为进一步验证所提方法的有效性，我们将三个低光数据集混合在一起，构建了一个具有更宽亮度分布的新数据集。与现有的11个代表性方法相比，在六个参考和非参考评估指标上，我们提出的方法取得了新的SOTA（State-of-the-Art）性能表现，特别是在PSNR方面，比现有最优方法提高了0.9 dB。
URL
https://arxiv.org/abs/2504.02362
