Response of Seed Germination and Seedling Growth of Six Desert Shrubs to Different Moisture Levels under Greenhouse Conditions.

Moisture is the most important environmental factor limiting seed regeneration of shrubs in desert areas. Therefore, understanding the effects of moisture changes on seed germination, morphological and physiological traits of shrubs is essential for vegetation restoration in desert areas. In March to June 2023, in a greenhouse using the potting method, we tested the effects of soil moisture changes (5%, 10%, 15%, 20% and 25%) on seed germination and seedling growth of six desert shrubs (Zygophyllum xanthoxylum, Nitraria sibirica, Calligonum mongolicum, Corethrodendron scoparium, Caragana korshinskii, and Corethrodendron fruticosu). Results showed that (1) seed germination percent and vigor index were significantly higher at 15 and 20% soil moisture content than at 5 and 10%; (2) shoot length, primary root length, specific leaf area and biomass of seedlings were significantly higher in the 15% and 20% soil moisture content treatments than in the 5% and 10% treatments; (3) superoxide dismutase activity (SOD) and soluble protein content (SP) decreased with decreasing soil water content, while peroxidase activity (POD) and catalase activity (CAT) showed a decreasing and then increasing trend with increasing soil water content; (4) the six seeds and seedling of shrubs were ranked in order of their survivability in response to changes in soil moisture: Caragana korshinskii > Zygophyllum xanthoxylum > Calligonum mongolicum > Corethrodendron scoparium > Corethrodendron fruticosu > Nitraria sibirica. Our study shows that shrub seedlings respond to water changes by regulating morphological and physiological traits together. More importantly, we found that C. korshinskii, Z. xanthoxylum and C. mongolicum were more survivable when coping with water deficit or extreme precipitation. The results of the study may provide a reference for the selection and cultivation of similar shrubs in desert areas under frequent extreme droughts in the future.

Exploiting Raw Images for Real-Scene Super-Resolution.

Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single image super-resolution, most algorithms only perform well on synthetic data, which limits their applications in real scenarios. In this paper, we study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images. We focus on two issues of existing super-resolution algorithms: lack of realistic training data and insufficient utilization of visual information obtained from cameras. To address the first issue, we propose a method to generate more realistic training data by mimicking the imaging process of digital cameras. For the second issue, we develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images. In addition, we propose a dense channel-attention block for better image restoration as well as a learning-based guided filter network for effective color correction. Our model is able to generalize to different cameras without deliberately training on images from specific camera types. Extensive experiments demonstrate that the proposed algorithm can recover fine details and clear structures, and achieve high-quality results for single image super-resolution in real scenes.