The multi-label recognition of damaged waste bottles has important significance in environmental protection. However, most of the previous methods are known for their poor performance, especially in regards to damaged waste bottle classification. In this paper, we propose the use of a serial attention frame (SAF) to overcome the mentioned drawback. The proposed network architecture includes the following three parts: a residual learning block (RB), a mixed attention block (MAB), and a self-attention block (SAB). The RB uses ResNet to pretrain the SAF to extract more detailed information. To address the effect of the complex background of waste bottle recognition, a serial attention mechanism containing MAB and SAB is presented. MAB is used to extract more salient category information via the simultaneous use of spatial attention and channel attention. SAB exploits the obtained features and its parameters to enable the diverse features to improve the classification results of waste bottles. The experimental results demonstrate that our proposed model exhibited good recognition performance in the collected waste bottle datasets, with eight labels of three classifications, i.e., the color, whether the bottle was damage, and whether the wrapper had been removed, as well as public image classification datasets.

High-quality images have an important effect on high-level tasks. However, due to human factors and camera hardware, digital devices collect low-resolution images. Deep networks can effectively restore these damaged images via their strong learning abilities. However, most of these networks depended on deeper architectures to enhance clarities of predicted images, where single features cannot deal well with complex screens. In this paper, we propose a dual super-resolution CNN (DSRCNN) to obtain high-quality images. DSRCNN relies on two sub-networks to extract complementary low-frequency features to enhance the learning ability of the SR network. To prevent a long-term dependency problem, a combination of convolutions and residual learning operation is embedded into dual sub-networks. To prevent information loss of an original image, an enhanced block is used to gather original information and obtained high-frequency information of a deeper layer via sub-pixel convolutions. To obtain more high-frequency features, a feature learning block is used to learn more details of high-frequency information. The proposed method is very suitable for complex scenes for image resolution. Experimental results show that the proposed DSRCNN is superior to other popular in SR networks. For instance, our DSRCNN has obtained improvement of 0.08 dB than that of MemNet on Set5 for ×3.

Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100,000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.