基于 FFT-ReLU 稀疏先验的盲图像去模糊
Blind Image Deblurring with FFT-ReLU Sparsity Prior
AI总结：
本文提出一种利用针对模糊核的FFT-ReLU稀疏先验的盲图像去模糊方法，能在广泛图像类型中有效去模糊，且推理速度比现有最优算法快两倍。
摘要：
盲图像去模糊是在没有模糊核先验知识的情况下，从模糊图像中恢复清晰图像的过程。这是一个小数据问题，因为关键挑战在于从单张图像或有限数据中估计未知的模糊程度，而不是从大型数据集中学习。解决方案严重依赖于开发能有效建模图像退化过程的算法。我们引入了一种利用针对模糊核的先验的方法，以在广泛的图像类型中实现有效去模糊。在我们广泛的经验分析中，我们的算法取得了与最先进的盲图像去模糊算法相竞争的结果，并且它提供了高达两倍的推理速度，使其成为一个高效的解决方案。
英文摘要：
Blind image deblurring is the process of recovering a sharp image from a blurred one without prior knowledge about the blur kernel. It is a small data problem, since the key challenge lies in estimating the unknown degrees of blur from a single image or limited data, instead of learning from large datasets. The solution depends heavily on developing algorithms that effectively model the image degradation process. We introduce a method that leverages a prior which targets the blur kernel to achieve effective deblurring across a wide range of image types. In our extensive empirical analysis, our algorithm achieves results that are competitive with the state-of-the-art blind image deblurring algorithms, and it offers up to two times faster inference, making it a highly efficient solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
