Multi-Dimension Modulation for Image Restoration with Dynamic Controllable Residual Learning
Motivation：现有的方法在实现两个目标之间的平滑过渡时受到限制，而实际输入图像可能包含不同种类的退化。传统的图像恢复深度学习方法(如图像去噪、去模糊和超分辨率)学习从退化图像空间到自然图像空间的确定性映射。对于给定的输入，这些方法中的大多数只能生成具有预先确定的恢复级别的固定输出。缺乏根据不同用户的喜好改变输出效果的灵活性。目前有的调制工作都是在一维上的，即是假设只有一个单一的退化类型。
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相关文章
- 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
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