A brief survey on deep learning based image super-resolution

Image super-resolution(SR)is an important technique for improving the resolution and quality of images.With the great progress of deep learning,image super-resolution achieves remarkable improvements recently.In this work,a brief survey on recent advances of deep learning based single image super-resolution methods is systematically described.The existing studies of SR techniques are roughly grouped into ten major categories.Besides,some other important issues are also introduced,such as publicly available benchmark datasets and performance evaluation metrics.Finally,this survey is concluded by highlighting four future trends.

Duplicated transmission based-resource scheduling for uplink grant-free SCMA system

Sparse code multiple access-based uplink grant-free transmission(SCMA-UGFT)has been proposed to realize ultra reliable and low latency communication(URLLC)in the fifth generation(5 G)system.Without the process of resource request and grant,users may collide in the same resource.To compensate the potential user performance decline,resource scheduling becomes a tough issue in the SCMA-UGFT system.This article proposes a duplicated transmission-based resource scheduling(DTBRS)scheme for SCMA-UGFT system by considering the URLLC scenario.Different from the existing schemes,not only one shared basic transmission units(BTUs)are allocated to a user equipment(UE)in the proposed DTBRS scheme for initial transmission to realize the duplicated transmission and to guarantee the transmission reliability.Besides,according to the proposed DTBRS scheme,one or two exclusive BTUs are assigned to a UE for retransmission to avoid the re-collision.At last,each packet is given a lifetime to limit the transmission latency to meet the URLLC latency requirement.The simulation demonstrates that the DTBRS scheme can achieve a better performance than the existing state-of-the-art scheme in terms of the average packet drop rate.