Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising

Shingo Takemoto, Kazuki Naganuma, Shunsuke Ono


The spatio-spectral total variation (SSTV) model has been widely used as an effective regularization of hyperspectral images (HSI) for various applications such as mixed noise removal. However, since SSTV computes local spatial differences uniformly, it is difficult to remove noise while preserving complex spatial structures with fine edges and textures, especially in situations of high noise intensity. To solve this problem, we propose a new TV-type regularization called Graph-SSTV (GSSTV), which generates a graph explicitly reflecting the spatial structure of the target HSI from noisy HSIs and incorporates a weighted spatial difference operator designed based on this graph. Furthermore, we formulate the mixed noise removal problem as a convex optimization problem involving GSSTV and develop an efficient algorithm based on the primal-dual splitting method to solve this problem. Finally, we demonstrate the effectiveness of GSSTV compared with existing HSI regularization models through experiments on mixed noise removal.


本論文では,Graph Spatio-spectral Total Variation(Graph SSTV, GSSTV)に基づくハイパースペクトル(HS)画像の新しいノイズ除去法を提案する.SSTV は,HS 画像のスペクトル相関を適切に捉える強力な正則化アプローチとして,混合ノイズ除去を含め広く応用されている.しかし,SSTV は空間的な正則化として単に垂直・水平方向の差を評価するだけであるため,エッジやテクスチャが混在するHS 画像の複雑な空間構造を保持したノイズ除去を行うには不十分であると考えられる.この問題を解決するため,HS 画像の空間構造を明示的に反映したグラフに基づく重み付き空間差分作用素をSSTV に統合することで,GSSTV を確立する.まず,与えられたノイズの多いHS 画像の全バンドを平均化したノイズ低減グレースケール画像(ガイド画像)を生成し,ガイド画像からグラフを構築する.次に,グラフを介して定義される空間差分作用素を用いてGSSTV を設計し,HS 画像のノイズ除去問題を,GSSTVを含む凸最適化問題で定式化する.さらに,この問題を解くための効率的なアルゴリズムを,主-双対近接分離法に基づいて開発する.混合ノイズ除去の実験を通して,SSTV を含む既存のHS 画像正則化モデルと比較し,GSSTV の有効性を実証する.


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S. Takemoto, K. Naganuma, and S. Ono, "Graph spatio-spectral total variation model for hyperspectral image denoising," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art. no. 6012405.



  author={Takemoto, Shingo and Naganuma, Kazuki and Ono, Shunsuke},

  journal={IEEE Geoscience and Remote Sensing Letters}, 

  title={Graph spatio-spectral total variation model for hyperspectral image denoising}, 






  note={{A}rt. no. 6012405}