DVDR-SRGAN: Differential Value Dense Residual Super-Resolution Generative Adversarial Network.

In the field of single-image super-resolution reconstruction, GAN can obtain the image texture more in line with the human eye. However, during the reconstruction process, it is easy to generate artifacts, false textures, and large deviations in details between the reconstructed image and the Ground Truth. In order to further improve the visual quality, we study the feature correlation between adjacent layers and propose a differential value dense residual network to solve this problem. We first use the deconvolution layer to enlarge the features, then extract the features through the convolution layer, and finally make a difference between the features before being magnified and the features after being extracted so that the difference can better reflect the areas that need attention. In the process of extracting the differential value, using the dense residual connection method for each layer can make the magnified features more complete, so the differential value obtained is more accurate. Next, the joint loss function is introduced to fuse high-frequency information and low-frequency information, which improves the visual effect of the reconstructed image to a certain extent. The experimental results on Set5, Set14, BSD100, and Urban datasets show that our proposed DVDR-SRGAN model is improved in terms of PSNR, SSIM, and LPIPS compared with the Bicubic, SRGAN, ESRGAN, Beby-GAN, and SPSR models.

Investigation of genetic relationships within three Miscanthus species using SNP markers identified with SLAF-seq.

BACKGROUND: Miscanthus, which is a leading dedicated-energy grass in Europe and in parts of Asia, is expected to play a key role in the development of the future bioeconomy. However, due to its complex genetic background, it is difficult to investigate phylogenetic relationships in this genus. Here, we investigated 50 Miscanthus germplasms: 1 female parent (M. lutarioriparius), 30 candidate male parents (M. lutarioriparius, M. sinensis, and M. sacchariflorus), and 19 offspring. We used high-throughput Specific-Locus Amplified Fragment sequencing (SLAF-seq) to identify informative single nucleotide polymorphisms (SNPs) in all germplasms. RESULTS: We identified 257,889 SLAF tags, of which 87,162 were polymorphic. Each tag was 264-364 bp long. The obtained 724,773 population SNPs were used to investigate genetic relationships within three species of Miscanthus. We constructed a phylogenetic tree of the 50 germplasms using the obtained SNPs and grouped them into two clades: one clade comprised of M. sinensis alone and the other one included the offspring, M. lutarioriparius, and M. sacchariflorus. Genetic cluster analysis had revealed that M. lutarioriparius germplasm C3 was the most likely male parent of the offspring. CONCLUSIONS: As a high-throughput sequencing method, SLAF-seq can be used to identify informative SNPs in Miscanthus germplasms and to rapidly characterize genetic relationships within this genus. Our results will support the development of breeding programs with the focus on utilizing Miscanthus cultivars with elite biomass- or fiber-production potential for the developing bioeconomy.