Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

Takahiro Kudo, Takanori Fujisawa, Takuro Yamaguchi, Masaaki Ikehara

Research output: Contribution to journalConference articlepeer-review

Abstract

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Original languageEnglish
JournalIS and T International Symposium on Electronic Imaging Science and Technology
Volume2020
Issue number10
DOIs
Publication statusPublished - 2020 Jan 26
Event18th Image Processing: Algorithms and Systems Conference, IPAS 2020 - Burlingame, United States
Duration: 2020 Jan 262020 Jan 30

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

Fingerprint Dive into the research topics of 'Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network'. Together they form a unique fingerprint.

Cite this