A Coded Aperture for Watermark Extraction from Defocused Images

Hiroki Hamasaki, Shingo Takeshita, Kentaro Nakai, Toshiki Sonoda, Hiroshi Kawasaki, Hajime Nagahara, Satoshi Ono

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Barcodes and 2D codes are widely used for various purposes, such as electronic payments and product management. Special code readers, and consumer smartphones can be used to scan codes; thus concerns about fraud and authenticity are important. Embedding watermarks in 2D codes, which allows simultaneous recognition and tamper detection by simply analyzing the captured pattern without requiring an additional device is considered a promising solution. However, smartphone cameras frequently suffer misfocus especially if the target object is too close to the lens, which makes the captured image defocused and results in failure to read watermarks. In this paper, we propose the use of a coded aperture imaging technique to recover watermarks. We have designed a coded aperture that is robust against defocus blur by optimizing the aperture pattern using a genetic algorithm. In addition, we have developed a programmable coded aperture that includes an actual optical process that works in an optimization loop; thus, the complicated effects of the optical aberrations can be considered. Experimental results demonstrate that the proposed method can extend the depth of field for watermark extraction to 3.1 times wider than that of a general circular aperture.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
EditorsGreg Mori, C.V. Jawahar, Konrad Schindler, Hongdong Li
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783030208752
Publication statusPublished - 2019
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: Dec 2 2018Dec 6 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11366 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Asian Conference on Computer Vision, ACCV 2018

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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