View Adaptive Light Field Deblurring Networks with Depth Perception
This addresses the problem of restoring blurred light field images for applications like computational photography, but it is incremental as it builds on existing deblurring approaches.
The paper tackles light field deblurring by designing a network that adapts to view-specific blur and depth variations, achieving better results than state-of-the-art methods on synthetic and real images.
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.