3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
1. 摘要 作者設計了一個 QRU3D 塊來對高光譜圖像進行去噪，其中的 3D 卷積負責提取空間和光譜的結構相關性，而近似循環池化函數則用來捕獲光譜方向的全局相關性。 此外，作者還引入了交替方向結構，以消除單向的因果關係，並且無需額外的計算成本。 2. 方法介紹 2.1. QRU3D QRU3D 塊的結構如上圖所示，包含兩個主要步驟：3D 卷積和近似循環池化。其中輸入 I I I 的大小 1 ∗
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相關文章
- 1. 《ADRN: ATTENTION-BASED DEEP RESIDUAL NETWORK FOR HYPERSPECTRAL IMAGE DENOISING》閱讀筆記
- 2. A new deep convolutional neural network for fast hyperspectral image classification Review
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- 4. Paper | Dynamic Residual Dense Network for Image Denoising
- 5. Generative Adversarial Networks for Hyperspectral Image Classification
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- 9. Deep Learning for Hyperspectral Image Classification:An Overview
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