• Acta Optica Sinica
  • Vol. 40, Issue 22, 2210003 (2020)
Tao Zhang1、2, Qin Zeng1、2、*, Wenli Du1、2, and Hao Wang1、2
Author Affiliations
  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  • 2Texas Instruments DSP Joint Lab, Tianjin University, Tianjin 300072, China
  • show less
    DOI: 10.3788/AOS202040.2210003 Cite this Article Set citation alerts
    Tao Zhang, Qin Zeng, Wenli Du, Hao Wang. Regional Backlight Brightness Extraction Algorithm Based on Deep Learning[J]. Acta Optica Sinica, 2020, 40(22): 2210003 Copy Citation Text show less

    Abstract

    Based on the analysis of the advantages and disadvantages of the existing algorithms, various types of images are considered as far as possible. Based on the principle prototype of light emitting diode-liquid crystal display direct-down image and video display, a reliable and effective data measurement method based on regional backlight brightness extraction is proposed, and an efficient and practical backlight brightness extraction method based on deep learning is proposed. The method is based on the neural network with a multi-layer down sampling structure to extract the image features and obtain the optimal backlight. The experimental results show that the method can improve the image display quality and expand the dynamic range of images. The experimental results of network structures with or without bypass verify the superiority and effectiveness of the proposed method.
    Tao Zhang, Qin Zeng, Wenli Du, Hao Wang. Regional Backlight Brightness Extraction Algorithm Based on Deep Learning[J]. Acta Optica Sinica, 2020, 40(22): 2210003
    Download Citation