Multi-channel residual network model for accurate estimation of spatially-varying and depth-dependent defocus kernels

Yanpeng Cao, Zhangyu Ye, Zewei He, Jiangxin Yang*, Yanlong Cao, Christel-loic Tisse, Michael Ying Yang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
48 Downloads (Pure)


Digital projectors have been increasingly utilized in various commercial and scientific applications. However, they are prone to the out-of-focus blurring problem since their depth-of-fields are typically limited. In this paper, we explore the feasibility of utilizing a deep learning-based approach to analyze the spatially-varying and depth-dependent defocus properties of digital projectors. A multimodal displaying/imaging system is built for capturing images projected at various depths. Based on the constructed dataset containing well-aligned in-focus, out-of-focus, and depth images, we propose a novel multi-channel residual deep network model to learn the end-to-end mapping function between the in-focus and out-of-focus image patches captured at different spatial locations and depths. To the best of our knowledge, it is the first research work revealing that the complex spatially-varying and depth-dependent blurring effects can be accurately learned from a number of real-captured image pairs instead of being hand-crafted as before. Experimental results demonstrate that our proposed deep learning-based method significantly outperforms the state-of-the-art defocus kernel estimation techniques and thus leads to better out-of-focus compensation for extending the dynamic ranges of digital projectors.
Original languageEnglish
Pages (from-to)2263-2275
Number of pages13
JournalOptics express
Issue number2
Publication statusPublished - 15 Jan 2020




Dive into the research topics of 'Multi-channel residual network model for accurate estimation of spatially-varying and depth-dependent defocus kernels'. Together they form a unique fingerprint.

Cite this