Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution
本文提出了一個低頻率紋理在高複雜度網絡訓練下會過擬合導致效果變差的問題，而現行網絡大多都忽視了這個問題將低頻紋理與高頻紋理一起輸入到深層複雜的網絡中 爲了解決這個問題這篇論文對紋理進行了分層處理總體框架分爲三個部分分別爲淺層特徵提取、分層處理不同頻率的特徵、最後重構生成HR圖片 其中這篇論文最主要的就是如何將高頻與低頻信息分開處理，首先將淺特徵提取曾得到的特徵圖輸入第一個BRM塊中經過反捲積或亞像
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相關文章
- 1. Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution
- 2. A Recursive Restoration Model for Single-Image Super-Resolution(EBRN ICCV2019)
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- 5. 論文解讀之MemNet: A Persistent Memory Network for Image Restoration
- 6. 《Dense Residual Network for Retinal Vessel Segmentation》
- 7. Paper | Dynamic Residual Dense Network for Image Denoising
- 8. 人羣密度估計--Learning a perspective-embedded deconvolution network for crowd counting
- 9. Residual attention network for image classification
- 10. A Deep One-class Model for Network Anomaly Detection