论文标题
低光图像上的类型曲线估计和视网膜融合
KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image
论文作者
论文摘要
弱光图像通常会遭受噪音和颜色失真的影响。由于图像噪声和色差，对象检测，语义分割，实例分割和其他任务在使用弱光图像时具有挑战性。我们还发现，常规的视网膜理论在调整低光任务的图像时失去了信息。针对上述问题，本文提出了一种用于低照明增强的算法。所提出的方法（类型）使用光曲线估计模块来增强视网膜分解图像中的照明图，从而提高整体图像亮度。还提出了照明图和反射图融合模块来恢复图像细节并减少细节损失。此外，应用电视（总变化）损耗函数以消除噪声。我们的方法在GLADNET数据集上进行了培训，该数据集以其低光图像的多样化集合而闻名，对低光数据集进行了测试，并使用Exdark数据集进行了下游任务的评估，并以19.7216的PSNR为19.7216，SSIM为0.8213。
Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a TV(total variation) loss function was applied to eliminate noise. Our method was trained on the GladNet dataset, known for its diverse collection of low-light images, tested against the Low-Light dataset, and evaluated using the ExDark dataset for downstream tasks, demonstrating competitive performance with a PSNR of 19.7216 and SSIM of 0.8213.
