DMTNet: Dynamic Multi-scale Network for Dual-pixel Images Defocus Deblurring with Transformer

Recent works achieve excellent results in defocus deblurring task based on dual-pixel data using convolutional neural network (CNN), while the scarcity of data limits the exploration and attempt of vision transformer in this task. In addition, the existing works use fixed parameters and network architecture to deblur images with different distribution and content information, which also affects the generalization ability of the model. In this paper, we propose a dynamic multi-scale network, named DMTNet, for dual-pixel images defocus deblurring. DMTNet mainly contains two modules: feature extraction module and reconstruction module. The feature extraction module is composed of several vision transformer blocks, which uses its powerful feature extraction capability to obtain richer features and improve the robustness of the model. The reconstruction module is composed of several Dynamic Multi-scale Sub-reconstruction Module (DMSSRM). DMSSRM can restore images by adaptively assigning weights to features from different scales according to the blur distribution and content information of the input images. DMTNet combines the advantages of transformer and CNN, in which the vision transformer improves the performance ceiling of CNN, and the inductive bias of CNN enables transformer to extract more robust features without relying on a large amount of data. DMTNet might be the first attempt to use vision transformer to restore the blurring images to clarity. By combining with CNN, the vision transformer may achieve better performance on small datasets. Experimental results on the popular benchmarks demonstrate that our DMTNet significantly outperforms state-of-the-art methods

SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution

Transformer-based methods have achieved impressive image restoration performance due to their capacities to model long-range dependency compared to CNN-based methods. However, advances like SwinIR adopts the window-based and local attention strategy to balance the performance and computational overhead, which restricts employing large receptive fields to capture global information and establish long dependencies in the early layers. To further improve the efficiency of capturing global information, in this work, we propose SwinFIR to extend SwinIR by replacing Fast Fourier Convolution (FFC) components, which have the image-wide receptive field. We also revisit other advanced techniques, i.e, data augmentation, pre-training, and feature ensemble to improve the effect of image reconstruction. And our feature ensemble method enables the performance of the model to be considerably enhanced without increasing the training and testing time. We applied our algorithm on multiple popular large-scale benchmarks and achieved state-of-the-art performance comparing to the existing methods. For example, our SwinFIR achieves the PSNR of 32.83 dB on Manga109 dataset, which is 0.8 dB higher than the state-of-the-art SwinIR method

Rain-induced changes in soil CO2 flux and microbial community composition in a tropical forest of China

Rain-induced soil CO2 pulse, a rapid excitation in soil CO2 flux after rain, is ubiquitously observed in terrestrial ecosystems, yet the underlying mechanisms in tropical forests are still not clear. We conducted a rain simulation experiment to quantify rain-induced changes in soil CO2 flux and microbial community composition in a tropical forest. Soil CO2 flux rapidly increased by ~83% after rains, accompanied by increases in both bacterial (~51%) and fungal (~58%) Phospholipid Fatty Acids (PLFA) biomass. However, soil CO2 flux and microbial community in the plots without litters showed limited response to rains. Direct releases of CO2 from litter layer only accounted for ~19% increases in soil CO2 flux, suggesting that the leaching of dissolved organic carbon (DOC) from litter layer to the topsoil is the major cause of rain-induced soil CO2 pulse. In addition, rain-induced changes in soil CO2 flux and microbial PLFA biomass decreased with increasing rain sizes, but they were positively correlated with litter-leached DOC concentration rather than total DOC flux. Our findings reveal an important role of litter-leached DOC input in regulating rain-induced soil CO2 pulses and microbial community composition, and may have significant implications for CO2 losses from tropical forest soils under future rainfall changes

SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection

Multi-modal fusion is increasingly being used for autonomous driving tasks, as images from different modalities provide unique information for feature extraction. However, the existing two-stream networks are only fused at a specific network layer, which requires a lot of manual attempts to set up. As the CNN goes deeper, the two modal features become more and more advanced and abstract, and the fusion occurs at the feature level with a large gap, which can easily hurt the performance. In this study, we propose a novel fusion architecture called skip-cross networks (SkipcrossNets), which combines adaptively LiDAR point clouds and camera images without being bound to a certain fusion epoch. Specifically, skip-cross connects each layer to each layer in a feed-forward manner, and for each layer, the feature maps of all previous layers are used as input and its own feature maps are used as input to all subsequent layers for the other modality, enhancing feature propagation and multi-modal features fusion. This strategy facilitates selection of the most similar feature layers from two data pipelines, providing a complementary effect for sparse point cloud features during fusion processes. The network is also divided into several blocks to reduce the complexity of feature fusion and the number of model parameters. The advantages of skip-cross fusion were demonstrated through application to the KITTI and A2D2 datasets, achieving a MaxF score of 96.85% on KITTI and an F1 score of 84.84% on A2D2. The model parameters required only 2.33 MB of memory at a speed of 68.24 FPS, which could be viable for mobile terminals and embedded devices

Responses of soil respiration and its temperature/moisture sensitivity to precipitation in three subtropical forests in southern China

Both long-term observation data and model simulations suggest an increasing chance of serious drought in the dry season and extreme flood in the wet season in southern China, yet little is known about how changes in precipitation pattern will affect soil respiration in the region. We conducted a field experiment to study the responses of soil respiration to precipitation manipulations – precipitation exclusion to mimic drought, double precipitation to simulate flood, and ambient precipitation as control (abbr. EP, DP and AP, respectively) – in three subtropical forests in southern China. The three forest sites include Masson pine forest (PF), coniferous and broad-leaved mixed forest (MF) and monsoon evergreen broad-leaved forest (BF). Our observations showed that altered precipitation strongly influenced soil respiration, not only through the well-known direct effects of soil moisture on plant and microbial activities, but also by modification of both moisture and temperature sensitivity of soil respiration. In the dry season, soil respiration and its temperature sensitivity, as well as fine root and soil microbial biomass, showed rising trends with precipitation increases in the three forest sites. Contrarily, the moisture sensitivity of soil respiration decreased with precipitation increases. In the wet season, different treatments showed different effects in three forest sites. The EP treatment decreased fine root biomass, soil microbial biomass, soil respiration and its temperature sensitivity, but enhanced soil moisture sensitivity in all three forest sites. The DP treatment significantly increased soil respiration, fine root and soil microbial biomass in the PF only, and no significant change was found for the soil temperature sensitivity. However, the DP treatment in the MF and BF reduced soil temperature sensitivity significantly in the wet season. Our results indicated that soil respiration would decrease in the three subtropical forests if soil moisture continues to decrease in the future. More rainfall in the wet season could have limited effect on the response of soil respiration to the rising of temperature in the BF and MF

Bryophyte diversity is related to vascular plant diversity and microhabitat under disturbance in karst caves

Plant diversity, habitat properties, and their relationships in karst caves remain poorly understood. We surveyed vascular plant and bryophyte diversities and measured the habitat characteristics in six karst caves in south China with different disturbance histories (one had been disturbed by poultry feeding, three had been disturbed by tourism, and two were undisturbed). The plant diversity differences among the six caves were analyzed using cluster analysis, and the relationships of plant diversity and microhabitat were assessed using canonical correspondence analysis. We found a total of 43 angiosperm species from 27 families, 20 lycophyte and fern species from 9 families, and 20 species of bryophytes from 13 families in the six caves. Habitat characteristics including light intensity, air relative humidity, air temperature, and soil properties varied among the caves. The plant diversity in karst caves was not rich, but the species composition was unique. The caves with high disturbance had the lowest species richness, numbers of individuals, and Shannon-Wiener diversity indices but the highest Simpson’s dominance indices. The caves with less disturbance had the highest numbers of species, numbers of individuals, and Shannon-Wiener diversity indices but the lowest Simpson’s dominance indices. The disturbed caves were often dominated by drought-tolerant, tenacious mosses (bryophytes), while the relatively undisturbed caves contained abundant liverworts (bryophytes), which were better adapted to humid environments. Plant diversity in karst caves was closely related to habitat heterogeneity, light and water status, and nutrient availability. Tourism and poultry farming were associated with the degradation of vegetation in some karst caves. Protecting and restoring bryophytes might facilitate the settlement, growth, and succession of vascular plants in karst caves. Bryophytes can be used as indicators of overall plant diversity and restoration status in karst caves

Stimulation of ammonia oxidizer and denitrifier abundances by nitrogen loading: Poor predictability for increased soil N2O emission

Unprecedented nitrogen (N) inputs into terrestrial ecosystems have profoundly altered soil N cycling. Ammonia oxidizers and denitrifiers are the main producers of nitrous oxide (N2O), but it remains unclear how ammonia oxidizer and denitrifier abundances will respond to N loading and whether their responses can predict N-induced changes in soil N2O emission. By synthesizing 101 field studies worldwide, we showed that N loading significantly increased ammonia oxidizer abundance by 107% and denitrifier abundance by 45%. The increases in both ammonia oxidizer and denitrifier abundances were primarily explained by N loading form, and more specifically, organic N loading had stronger effects on their abundances than mineral N loading. Nitrogen loading increased soil N2O emission by 261%, whereas there was no clear relationship between changes in soil N2O emission and shifts in ammonia oxidizer and denitrifier abundances. Our field-based results challenge the laboratory-based hypothesis that increased ammonia oxidizer and denitrifier abundances by N loading would directly cause higher soil N2O emission. Instead, key abiotic factors (mean annual precipitation, soil pH, soil C:N ratio, and ecosystem type) explained N-induced changes in soil N2O emission. Altogether, these findings highlight the need for considering the roles of key abiotic factors in regulating soil N transformations under N loading to better understand the microbially mediated soil N2O emission

Effects of Heat Shock on Photosynthetic Properties, Antioxidant Enzyme Activity, and Downy Mildew of Cucumber (Cucumis sativus L.)

Heat shock is considered an abiotic stress for plant growth, but the effects of heat shock on physiological responses of cucumber plant leaves with and without downy mildew disease are still not clear. In this study, cucumber seedlings were exposed to heat shock in greenhouses, and the responses of photosynthetic properties, carbohydrate metabolism, antioxidant enzyme activity, osmolytes, and disease severity index of leaves with or without the downy mildew disease were measured. Results showed that heat shock significantly decreased the net photosynthetic rate, actual photochemical efficiency, photochemical quenching coefficient, and starch content. Heat shock caused an increase in the stomatal conductance, transpiration rate, antioxidant enzyme activities, total soluble sugar content, sucrose content, soluble protein content and proline content for both healthy leaves and downy mildew infected leaves. These results demonstrate that heat shock activated the transpiration pathway to protect the photosystem from damage due to excess energy in cucumber leaves. Potential resistance mechanisms of plants exposed to heat stress may involve higher osmotic regulation capacity related to an increase of total accumulations of soluble sugar, proline and soluble protein, as well as higher antioxidant enzymes activity in stressed leaves. Heat shock reduced downy mildew disease severity index by more than 50%, and clearly alleviated downy mildew development in the greenhouses. These findings indicate that cucumber may have a complex physiological change to resist short-term heat shock, and suppress the development of the downy mildew disease