Multi-Dimension Modulation for Image Restoration with Dynamic Controllable Residual Learning
Motivation：現有的方法在實現兩個目標之間的平滑過渡時受到限制，而實際輸入圖像可能包含不同種類的退化。傳統的圖像恢復深度學習方法(如圖像去噪、去模糊和超分辨率)學習從退化圖像空間到自然圖像空間的確定性映射。對於給定的輸入，這些方法中的大多數只能生成具有預先確定的恢復級別的固定輸出。缺乏根據不同用戶的喜好改變輸出效果的靈活性。目前有的調製工作都是在一維上的，即是假設只有一個單一的退化類型。
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相關文章
- 1. CFSNet: Toward a Controllable Feature Space for Image Restoration ICCV2019
- 2. Discriminative Transfer Learning for General Image Restoration
- 3. Paper | Dynamic Residual Dense Network for Image Denoising
- 4. Deep Residual Learning for Image Recognition
- 5. ResNet--Deep Residual Learning for Image Recognition
- 6. [ResNet]Deep Residual Learning for Image Recognition
- 7. ResNet —— Deep Residual Learning for Image Recognition
- 8. Deep Residual Learning for Image Recognition （zh）
- 9. ResNet(deep residual learning for image recognition)
- 10. ResNet——Deep Residual Learning for Image Recognition