论文标题
基于超网络的自适应图像恢复
Hypernetwork-Based Adaptive Image Restoration
论文作者
论文摘要
自适应图像恢复模型可以在推理时恢复具有不同降解水平的图像，而无需重新训练模型。我们提出了一种高度准确的方法，可以显着减少参数数量。与现有方法相反，我们的方法可以使用单个固定尺寸模型恢复图像，而不管降解级别的数量如何。在流行的数据集上，我们的方法在各种图像恢复任务（包括Denoising，dejpeg and dejpeg and Super-resolution）方面产生最新的结果。
Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model. We present an approach that is highly accurate and allows a significant reduction in the number of parameters. In contrast to existing methods, our approach can restore images using a single fixed-size model, regardless of the number of degradation levels. On popular datasets, our approach yields state-of-the-art results in terms of size and accuracy for a variety of image restoration tasks, including denoising, deJPEG, and super-resolution.
