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Show-through removal with sparsity-based blind deconvolution.

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  • 1Degree Programs in Systems and Information Engineering, Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan.

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Summary
This summary is machine-generated.

This study introduces a novel method for removing show-through from scanned documents by reformulating it as a nuclear norm minimization problem. The technique effectively cleans scanned images, improving document clarity.

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Area of Science:

  • Digital Image Processing
  • Computer Vision

Background:

  • Scanning double-sided documents causes show-through, where text from one side appears on the other.
  • Existing methods for show-through removal are limited.

Purpose of the Study:

  • To propose a new and effective method for show-through removal from scanned documents.
  • To leverage principles from Blind Image Deconvolution for this task.

Main Methods:

  • The method reformulates show-through removal as a nuclear norm minimization problem.
  • It utilizes two scanned images (front and back) as input.
  • A cost function combines data, nuclear norm, and Total Variation regularization terms.
  • The problem is solved using Accelerated Proximal Gradient and Singular Value Projection.

Main Results:

  • The proposed method demonstrates effective show-through removal.
  • Simulations and real-world experiments validate the performance.
  • The approach is computationally efficient and easy to implement.

Conclusions:

  • The nuclear norm minimization approach offers a robust solution for scanned document show-through removal.
  • This method enhances the quality of scanned documents by eliminating unwanted show-through.
  • The technique shows promise for practical applications in document digitization.