Abstract
The Light Field (LF) deblurring task is a challenging problem as the blur images are caused by different reasons like the camera shake and the object motion. The single image deblurring method is a possible way to solve this problem. However, since it deals with each view independently and cannot effectively utilize and maintain the LF structure, the restoration effect is usually not ideal. Besides, the LF blur is more complex because the degree is affected by the views and depth. Therefore, we carefully designed a novel LF deblurring network based on the LF blur characteristics. On one hand, since the blur degree varies a lot in different views, we design a novel view adaptive spatial convolution to deblur blurred LFs, which calculates the exclusive convolution kernel for each view. On the other hand, because the blur degree also varies with the depth of the object, a depth perception view attention is designed to deblur different depth areas by selectively integrating information from different views. Besides, we introduce an angular position embedding to maintain the LF structure better, which ensures the model correctly restores the view information. Quantitative and qualitative experimental results on synthetic and real images show that the deblurring effect of our method is better than other state-of-the-art methods.
Abstract (translated)
光场(LF)去模糊任务是一个挑战性的问题,因为模糊图像可能是由于各种不同的原因,如相机震动和物体运动引起的。单张图像去模糊方法可能是解决这个问题的一种可能方法。然而,由于它独立处理每个视图,并且无法有效地利用和维护LF结构,恢复效果通常不太理想。此外,LF模糊的复杂度因为程度受视图和深度的影响。因此,我们 carefully designed a novel LF去模糊网络基于LF模糊特性。一方面,因为不同视图的模糊程度在很多方面都有很大的差异,我们设计了一种新型视角自适应空间卷积去模糊模糊的LF,计算每个视图的独家卷积核。另一方面,因为模糊程度也与物体深度有关,我们设计了一种深度感知视图注意力,通过选择性集成来自不同视图的信息,去模糊不同深度区域。此外,我们引入了角度位置嵌入来更好地维护LF结构,以确保模型正确恢复视图信息。在模拟和真实图像上的定量和定性实验结果表明,我们的方法去模糊效果比其他先进的方法更好。
URL
https://arxiv.org/abs/2303.06860
