Split-and-Merge Algorithm for Motion Deblurring Based on DeblurGAN

Motion blur is a highly challenging problem in computer vision literature. Due to the ill-posed nature and the camera shake, the relative motion between the camera and the object in 3D space induces a spatially varying blurring effect over the entire image. This paper proposes split-and-merge algorithm based on DeblurGAN to make blurred image become relatively clear. For the same motion blur image, usually blurred degree of different parts is not the same. Therefore split-and-merge algorithm firstly split a blurred image into several parts and measure blurred degree for each part. Then the algorithm do deblurring for different times according to different blurred degree until all parts reach the acceptable degree. Finally the algorithm merge all parts into one image. The experiment results show this algorithm can help enhance sharpness of motion blur picture than original DeblurGAN

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field