Multiview high dynamic range image synthesis using fuzzy broad learning system

Hongbin Guo, Bin Sheng, Ping Li, C. L. Philip Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2735-2747
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number5
Publication statusPublished - May 2021


  • Fuzzy broad learning system (FBLS)
  • High dynamic range (HDR) image
  • Multiview synthesis

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Multiview high dynamic range image synthesis using fuzzy broad learning system'. Together they form a unique fingerprint.

Cite this