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マルチスケールアテンション特徴量を用いた水中画像品質復元のための適応的融合ベース深層学習フレームワーク

T Veeramakali1, Md Shohel Sayeed1, Sumendra Yogarayan2

  • 1Centre for Intelligent Cloud Computing, COE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, Malaka, 75450, Malaysia.

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まとめ

本研究では、効率的な水中画像復元のためのマルチスケールアテンション特徴量（ERUI-MSAF）モデルを導入します。ERUI-MSAFモデルは、水中画像の視認性と品質を効果的に向上させ、既存の方法を上回る性能を示します。

キーワード:
適応的双方向フィルタリング深層学習マルチスケールアテンション特徴量復元水中画像

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科学分野:

  • コンピュータビジョン
  • 画像処理
  • 深層学習

背景:

  • 水中画像は、ぼかし、低コントラスト、色ずれなどの劣化に悩まされています。
  • 水中画像の復元は、様々な実用的なアプリケーションにとって重要です。
  • 従来の画像復元手法は、複雑な水中画像の劣化に対処するのが困難です。

研究 の 目的:

  • 水中画像の復元のための効果的な手法を開発すること。
  • 水中画像の視認性と全体的な品質を向上させること。
  • 効率的な水中画像復元のためのマルチスケールアテンション特徴量（ERUI-MSAF）モデルを導入すること。

主な方法:

  • ノイズ低減と前処理のための適応的双方向フィルタリング（ABF）。
  • チャネルおよび空間アテンション特徴量を統合したERUI-MSAFモデル。
  • 空間特徴量のためのDeep Wavenet（DWN）とチャネル特徴量のためのEfficientNetの融合。

主要な成果:

  • ERUI-MSAFモデルは、情報量の多い特徴量と領域を適応的に強調します。
  • EUVPおよびUIEBデータセットで34.258および29.0073の優れたピーク信号対雑音比（PSNR）値を達成しました。
  • 既存のモデルと比較して、高い性能と計算効率を示しました。

結論:

  • 提案されたERUI-MSAFモデルは、水中画像の復元に効果的です。
  • マルチスケールアテンション特徴量の統合は、画質を大幅に向上させます。
  • この手法は、水中画像の向上に有望なソリューションを提供します。