Guo Hongbin, Sheng Bin, Li Ping, Chen C L Philip
IEEE Trans Cybern. 2021 May;51(5):2735-2747. doi: 10.1109/TCYB.2019.2934823. Epub 2021 Apr 15.
Compared with the normal low dynamic range (LDR) images, the high dynamic range (HDR) images provide more dynamic range and image details. Although the existing techniques for generating the HDR images have a good effect for static scenes, they usually produce artifacts on the HDR images for dynamic scenes. In recent years, some learning-based approaches are used to synthesize the HDR images and obtain good results. However, there are also many problems, including the deficiency of explaining and the time-consuming training process. In this article, we propose a novel approach to synthesize multiview HDR images through fuzzy broad learning system (FBLS). We use a set of multiview LDR images with different exposure as input and transfer corresponding Takagi-Sugeno (TS) fuzzy subsystems; then, the structure is expanded in a wide sense in the "enhancement groups" which transfer from the TS fuzzy rules with nonlinear transformation. After integrating fuzzy subsystems and enhancement groups with the trained-well weight, the HDR image is generated. In FBLS, applying the incremental learning algorithm and the pseudoinverse method to compute the weights can greatly reduce the training time. In addition, the fuzzy system has better interpretability. In the learning process, IF-THEN fuzzy rules can effectively help the model to detect the artifacts and reject them in the final HDR result. These advantages solve the problem of existing deep-learning methods. Furthermore, we set up a new dataset of multiview LDR images with corresponding HDR ground truth to train our system. Our experimental results show that our system can synthesize high-quality multiview HDR images, which has a higher training speed than other learning methods.
与普通的低动态范围（LDR）图像相比，高动态范围（HDR）图像提供了更大的动态范围和图像细节。尽管现有的HDR图像生成技术在静态场景中效果良好，但在动态场景的HDR图像上通常会产生伪影。近年来，一些基于学习的方法被用于合成HDR图像并取得了良好的效果。然而，也存在许多问题，包括解释性不足和训练过程耗时。在本文中，我们提出了一种通过模糊广义学习系统（FBLS）合成多视图HDR图像的新方法。我们使用一组具有不同曝光的多视图LDR图像作为输入，并传递相应的高木-关野（TS）模糊子系统；然后，在从具有非线性变换的TS模糊规则传递而来的“增强组”中进行广义的结构扩展。在将模糊子系统和增强组与训练良好的权重进行整合后，生成HDR图像。在FBLS中，应用增量学习算法和伪逆方法来计算权重可以大大减少训练时间。此外，模糊系统具有更好的可解释性。在学习过程中，IF-THEN模糊规则可以有效地帮助模型检测伪影并在最终的HDR结果中排除它们。这些优点解决了现有深度学习方法的问题。此外，我们建立了一个新的多视图LDR图像数据集以及相应的HDR地面真值来训练我们的系统。我们的实验结果表明，我们的系统可以合成高质量的多视图HDR图像，并且其训练速度比其他学习方法更高。
