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赤外線画像ノイズ除去アルゴリズム:ウェーブレット変換と自己注意機構に基づく

Hongmei Li1, Yang Zhang1, Luxia Yang1

  • 1College of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China.

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|January 28, 2026
PubMed
まとめ

本研究では、赤外線画像の品質を向上させるために、ウェーブレット変換強化赤外線ノイズ除去モデル(WTEIDM)を提案します。WTEIDMは、重要な詳細を保持しながら効果的にノイズを除去し、既存の方法を上回る性能を発揮します。

キーワード:
赤外線画像ノイズ除去マルチスケールゲート付き線形ユニット並列ハイブリッドアテンショントランスフォーマーウェーブレット変換自己注意

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科学分野:

  • コンピュータビジョン
  • 画像処理
  • 信号処理

背景:

  • 赤外線画像は複雑なノイズに悩まされており、ターゲット検出と分析を妨げています。
  • 既存のノイズ除去方法は、ノイズ削減と詳細保持のバランスを取るのに苦労しています。

研究 の 目的:

  • ノイズを効果的に除去し、画像の詳細を保持する高度な赤外線ノイズ除去モデルを提案すること。
  • 後続のタスクのために赤外線画像処理のパフォーマンスを向上させること。

主な方法:

  • ウェーブレット変換強化赤外線ノイズ除去モデル(WTEIDM)を開発しました。
  • ノイズと詳細の分離のためにウェーブレット変換自己注意(WTSA)モジュールを組み込みました。
  • 詳細キャプチャを強化するためにマルチスケールゲート付き線形ユニット(MSGLU)を利用しました。
  • 特徴融合を改善するために並列ハイブリッドアテンションモジュール(PHAM)を採用しました。

主要な成果:

  • WTEIDMは、最先端のノイズ除去アルゴリズムと比較して優れた性能を示しました。
  • 様々なノイズレベル(σ = 15、25、50)を持つ5つの赤外線データセットで評価されました。
  • より高いピーク信号対雑音比(PSNR)および構造的類似性指数(SSIM)メトリクスを達成しました。

結論:

  • 提案されたWTEIDMは、現在の赤外線ノイズ除去技術の限界を効果的に解決します。
  • このモデルは、さまざまなノイズ条件下で優れた汎化能力と堅牢性を示します。
  • WTEIDMは、下流アプリケーションの改善のために赤外線画像の品質を大幅に向上させます。