Semantic Information Supplementary Pyramid Network for Dynamic Scene Deblurring

Pyramid Scene Parsing Network — Supplymentary Material

Dynamic Scene Deblurring using a Locally Adaptive Linear Blur Model

State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes, since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as m...

Propagating semantic information in biochemical network models Supplementary Appendix

In this Supplementary Appendix, we first describe in detail which information can be propagated in models described in the Systems Biology Markup Language. We further show how the two propagation schemes discussed in the main text feature propagation and similarity propagation are mathematically related. Then, we discuss ways to normalise propagated similarities and explain how our scoring func...

Learning Similarity Metrics for Dynamic Scene Segmentation Supplementary material

The data from the SynthDB dataset used for training consists of the 99 sequences featuring 2 textures [1]. Each texture is labeled as one of these 12 classes: grass, jellyfish, pond, boiling, escalator, fire, river-far, river, steam, plant-a, plant-i, and sea-far. All results reported on the SynthDB and Dyntex datasets used a single scale, i.e. S=1. In our experiments, the use of multiple scale...

Deep Semantic Face Deblurring

In this paper, we present an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks (CNNs). As face images are highly structured and share several key semantic components (e.g., eyes and mouths), the semantic information of a face provides a strong prior for restoration. As such, we propose to incorporate global semantic priors as in...