Spherical Image Inpainting with Frame Transformation and Data-Driven Prior Deep Networks

Jianfei LI, Chaoyan HUANG, Raymond CHAN, Han FENG*, Michael K. NG, Tieyong ZENG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modeling, and medical imaging. It is nontrivial to extend an algorithm developed for flat images to the spherical ones. In this work, we focus on the challenging task of spherical image inpainting with a deep learning-based regularizer. Instead of a naive application of existing models for planar images, we employ a fast directional spherical Haar framelet transform and develop a novel optimization framework based on a sparsity assumption of the framelet transform. Furthermore, by employing progressive encoder-decoder architecture, a new and better-performed deep CNN denoiser is carefully designed and works as an implicit regular-izer. Finally, we use a plug-and-play method to handle the proposed optimization model, which can be implemented efficiently by training the CNN denoiser prior. Numerical experiments are conducted and show that the proposed algorithms can greatly recover damaged spherical images and achieve the best performance over purely using a deep learning denoiser and a plug-and-play model.

Original languageEnglish
Pages (from-to)1177-1194
Number of pages18
JournalSIAM Journal on Imaging Sciences
Issue number3
Publication statusPublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Society for Industrial and Applied Mathematics.


  • deep CNN
  • plug-and-play
  • spherical image inpainting


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