Most preexisting deep learning-based enhancers are incapable of adjusting brightness of enhanced images, due to constant convolutional kernels. To address this issue, we propose an enhancer based on Taylor expansion and fully dynamic convolution, which can flexibly adjust the level of the brightness. In this study, the retinex model is first modified to serve as a framework for the proposed enhancer. Next, Taylor expansion and the attention mechanism are applied to construct a backbone network based on the modified retinex model. Subsequently, a strategy of fully dynamic convolution is proposed to flexibly adjust the degree of the brightness. Specifically, a weight-bias learning network is designed to dynamically generate weight matrices which are fed to the backbone network to perform the dynamic convolution. Furthermore, local mean and variance are used as a supplemental term for our loss function to improve the performance of the proposed enhancer, while a method of simulating realistic low-light images is used for synthesizing training data to suppress noise. Comprehensive experiments demonstrate satisfactory performance of the proposed enhancer in improving the clarity of low-light images and adjusting the degree of the brightness flexibly.

由于恒定的卷积核，大多数现有的基于深度学习的增强器无法调整增强图像的亮度。为了解决这个问题，我们提出了一种基于泰勒展开和全动态卷积的增强器，可以灵活地调整亮度的水平。在这项研究中，首先修改了 retinex 模型以作为所提出的增强器的框架。接下来，基于修改后的 retinex 模型，应用泰勒展开和注意力机制构建骨干网络。随后，提出了一种全动态卷积的策略来灵活调整亮度的程度。具体来说，权重偏置学习网络旨在动态生成权重矩阵，这些权重矩阵被馈送到骨干网络以执行动态卷积。此外，局部均值和方差被用作我们的损失函数的补充术语，以提高所提出的增强器的性能，而模拟逼真的低光图像的方法用于合成训练数据以抑制噪声。综合实验表明，所提出的增强器在提高低光图像的清晰度和灵活调节亮度方面具有令人满意的性能。