Abstract
Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks. We show that when extracting patches of similar underlying blur is possible, jointly processing the stack of patches yields superior accuracy than handling them separately. Our collaborative scheme is implemented in a neural architecture with a pooling layer on the stack dimension. We present three practical patch extraction strategies for image sharpening, camera shake removal and optical aberration correction, and validate the proposed approach on both synthetic and real-world benchmarks. For each blur instance, the proposed collaborative strategy yields significant quantitative and qualitative improvements.
Abstract (translated)
模糊图像通常在图像域中在各种位置表现出类似的模糊，这是当今的无监督去模糊神经网络几乎无法捕捉到的特性。我们证明，当提取具有相似底层模糊的图案时，联合处理Stack of patches可以获得比单独处理更好的精度。我们的合作方案是在Stack dimension上有一个Pooling layer的神经网络架构中实现的。我们提出了三个实用的图案提取策略，用于图像增强、相机抖动去除和光学畸变纠正，并在不同的合成和实际基准点上进行了验证。对于每个模糊实例，我们提出的合作策略都带来了显著的数量和质量改进。
URL
https://arxiv.org/abs/2305.16034
