Saturated Reflection Detection for Reflection Removal Based on Convolutional Neural Network

Taichi Yoshida, Isana Funahashi, Naoki Yamashita, Masaaki Ikehara

Research output: Contribution to journalArticlepeer-review

Abstract

Single image reflection removal is a technique that removes undesirable reflections, which occur due to glass, from images. Various methods of reflection removal have been proposed, but unfortunately, they usually fail to remove reflections particularly with very high pixel values. In this paper, we define these saturated reflections and their characteristics, as well as discuss and propose a removal system. The proposed system detects areas of saturated reflections based on our proposed model of convolutional neural networks and restores them by a conventional method of image estimation. In our experiments, the proposed system shows better peak-signal-to-noise ratio scores and perceptual quality than conventional methods of reflection removal.

Original languageEnglish
Pages (from-to)39800-39809
Number of pages10
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • Single image reflection removal
  • convolutional neural network
  • image inpainting
  • reflection detection

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering

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