Calibrating an unfocused plenoptic camera based on parameters grouping and the light field structure point.

Accurately calibrating an unfocused plenoptic camera is essential to its applications. Rapid progress has been made in this area in the past decades. In this paper, detailed analysis is first performed toward the state-of-the-art projection model. Based on the analysis, parameters in the projection model are divided into two groups. Then, based on the parameter analysis, a new, to the best of our knowledge, form of the projection model, together with a new image feature light field structure point (LF-structure-point), is proposed. The LF-structure-point provides a completely non-redundant representation of the signal structure of the recorded light field raw data and induces a virtual space, "light field structure space," which is related to the real physical space by a 3D-to-3D projective transformation. The extracting algorithm of the LF-structure-point is also presented. Finally, based on the 3D-to-3D projective transformation and parameter grouping, a simple two-step calibration method using the LF-structure-point as the input data is then proposed and achieves satisfactory experimental results.

Occurrence, Characteristics, and qPCR-Based Identification of Pectobacterium versatile Causing Soft Rot of Chinese Cabbage in China.

Pectobacterium is one of the most important genera of phytopathogenic bacteria. It can cause soft-rot diseases on a wide range of plant species across the world. In this study, three Pectobacterium strains (KC01, KC02, and KC03) were isolated from soft-rotted Chinese cabbage in Beijing, China. These three strains were identified as Pectobacterium versatile based on phylogenetic analysis of Pectobacterium 16S ribosomal RNA, pmrA, and 504 Pectobacterium core genes, as well as a genomic average nucleotide identity analysis. Their biochemical characteristics were found to be similar to the P. versatile type strain ICMP9168T but differed in response to citric acid, stachyose, D-glucuronic acid, dextrin, and N-acetyl-ß-D-mannosamine. All of the tested P. versatile strains showed different carbohydrate utilization abilities compared with P. carotovorum and P. odoriferum, particularly in their ability to utilize D-arabitol, L-rhamnose, and L-serine. Under laboratory conditions, the maceration ability of P. versatile on Chinese cabbage was the highest at 28°C, compared with those at 13, 28, 23, and 33°C. Additionally, P. versatile could infect all of the 17 known Pectobacterium host plants, except for Welsh onion (Allium fistulosum). A SYBR Green quantitative PCR (qPCR) detection system was developed to distinguish P. versatile from other soft-rot bacteria based on the combined performance of melting curve (with a single melting peak at around 85°C) and fluorescence curve (with cycle threshold <30) when the bacterial genomic DNA concentration was in the range of 10 pg/µl to 10 ng/µl. This study is the first to report the presence of P. versatile on Chinese cabbage in China, as well as a specific and sensitive qPCR assay that can be used to quickly identify P. versatile. The work contributes to a better understanding of P. versatile and will facilitate the effective diagnosis of soft-rot disease, ultimately benefitting commercial crop production.

Validation of Sentinel-3A/3B Satellite Altimetry Wave Heights with Buoy and Jason-3 Data.

The validation of significant wave height (SWH) data measured by the Sentinel-3A/3B SAR Altimeter (SRAL) is essential for the application of the data in ocean wave monitoring, forecasting and wave climate studies. Sentinel-3A/3B SWH data are validated by comparisons with U. S. National Data Buoy Center (NDBC) buoys, using a spatial scale of 25 km and a temporal scale of 30 min, and with Jason-3 data at their crossovers, using a time difference of less than 30 min. The comparisons with NDBC buoy data show that the root-mean-square error (RMSE) of Sentinel-3A SWH is 0.30 m, and that of Sentinel-3B is no more than 0.31 m. The pseudo-Low-Resolution Mode (PLRM) SWH is slightly better than that of the Synthetic Aperture Radar (SAR) mode. The statistical analysis of Sentinel-3A/3B SWH in the bin of 0.5 m wave height shows that the accuracy of Sentinel-3A/3B SWH data decreases with increasing wave height. The analysis of the monthly biases and RMSEs of Sentinel-3A SWH shows that Sentinel-3A SWH are stable and have a slight upward trend with time. The comparisons with Jason-3 data show that SWH of Sentinel-3A and Jason-3 are consistent in the global ocean. Finally, the piecewise calibration functions are given for the calibration of Sentinel-3A/3B SWH. The results of the study show that Sentinel-3A/3B SWH data have high accuracy and remain stable.

Proposal of a Geometric Calibration Method Using Sparse Recovery to Remove Linear Array Push-Broom Sensor Bias.

The rational function model (RFM) is widely used in the most advanced Earth observation satellites, replacing the rigorous imaging model. The RFM method achieves the desired calibration performance when image distortion is caused by long-period errors. However, the calibration performance of the RFM method deteriorates when short-period errors-such as attitude jitter error-are present, and the insufficient and uneven ground control points (GCPs) can also lower the calibration precision of the RFM method. Hence, this paper proposes a geometric calibration method using sparse recovery to remove the linear array push-broom sensor bias. The most important issue regarding this method is that the errors related to the imaging process are approximated to the equivalent bias angles. By using the sparse recovery method, the number and distribution of GCPs needed are greatly reduced. Meanwhile, the proposed method effectively removes short-period errors by recognizing periodic wavy patterns in the first step of the process. The image data from Earth Observing 1 (EO-1) and the Advanced Land Observing Satellite (ALOS) are used as experimental data for the verification of the calibration performance of the proposed method. The experimental results indicate that the proposed method is effective for the sensor calibration of both satellites.

Compressed Sensing Radar Imaging: Fundamentals, Challenges, and Advances.

In recent years, sparsity-driven regularization and compressed sensing (CS)-based radar imaging methods have attracted significant attention. This paper provides an introduction to the fundamental concepts of this area. In addition, we will describe both sparsity-driven regularization and CS-based radar imaging methods, along with other approaches in a unified mathematical framework. This will provide readers with a systematic overview of radar imaging theories and methods from a clear mathematical viewpoint. The methods presented in this paper include the minimum variance unbiased estimation, least squares (LS) estimation, Bayesian maximum a posteriori (MAP) estimation, matched filtering, regularization, and CS reconstruction. The characteristics of these methods and their connections are also analyzed. Sparsity-driven regularization and CS based radar imaging methods represent an active research area; there are still many unsolved or open problems, such as the sampling scheme, computational complexity, sparse representation, influence of clutter, and model error compensation. We will summarize the challenges as well as recent advances related to these issues.

Regulating root structure of potted lettuce to magnify absorption from APP and UAN fertilizers.

Introduction: Improvement of root architecture is crucial to increasing nutrient acquisition. Methods: Two pot experiments were conducted to investigate the effects of different concentrations of urea ammonium nitrate solution (UAN) and ammonium polyphosphate (APP) on lettuce root architecture and the relationship between roots and nitrogen (N) and phosphorus (P) absorption. Results: The results showed that lettuce yield, quality, and root architecture were superior in the APP4 treatment compared to other P fertilizer treatments. The N480 treatment (480 mg N kg-1 UAN) significantly outperformed other N treatments in terms of root length, root surface area, and root volume. There were significant quantitative relationships between root architecture indices and crop uptake of N and P. The relationships between P uptake and root length and root surface area followed power functions. Crop N uptake was significantly linearly related to the length of fine roots with a diameter of <0.5 mm. Conclusion and discussion: The length of fine roots played a more prominent role in promoting N absorption, while overall root size was more important for P absorption. APP has a threshold of 9.3 mg P kg-1 for stimulating the root system. Above this threshold, a rapid increase in root absorption of P. UAN can promote extensive growth of fine roots with a diameter less than 0.5 mm. Applying appropriate rates of APP and limiting UAN application to less than 400 mg N kg-1 can improve root architecture to enhance N and P absorption by lettuce. These results highlight a new possibility to improve nutrients use efficiency while maintaining high yields.

Real-World Light Field Image Super-Resolution Via Degradation Modulation.

Recent years have witnessed the great advances of deep neural networks (DNNs) in light field (LF) image super-resolution (SR). However, existing DNN-based LF image SR methods are developed on a single fixed degradation (e.g., bicubic downsampling), and thus cannot be applied to super-resolve real LF images with diverse degradation. In this article, we propose a simple yet effective method for real-world LF image SR. In our method, a practical LF degradation model is developed to formulate the degradation process of real LF images. Then, a convolutional neural network is designed to incorporate the degradation prior into the SR process. By training on LF images using our formulated degradation, our network can learn to modulate different degradation while incorporating both spatial and angular information in LF images. Extensive experiments on both synthetically degraded and real-world LF images demonstrate the effectiveness of our method. Compared with existing state-of-the-art single and LF image SR methods, our method achieves superior SR performance under a wide range of degradation, and generalizes better to real LF images. Codes and models are available at https://yingqianwang.github.io/LF-DMnet/.

Unsupervised Degradation Representation Learning for Unpaired Restoration of Images and Point Clouds.

Restoration tasks in low-level vision aim to restore high-quality (HQ) data from their low-quality (LQ) observations. To circumvents the difficulty of acquiring paired data in real scenarios, unpaired approaches that aim to restore HQ data solely on unpaired data are drawing increasing interest. Since restoration tasks are tightly coupled with the degradation model, unknown and highly diverse degradations in real scenarios make learning from unpaired data quite challenging. In this paper, we propose a degradation representation learning scheme to address this challenge. By learning to distinguish various degradations in the representation space, our degradation representations can extract implicit degradation information in an unsupervised manner. Moreover, to handle diverse degradations, we develop degradation-aware (DA) convolutions with flexible adaption to various degradations to fully exploit the degrdation information in the learned representations. Based on our degradation representations and DA convolutions, we introduce a generic framework for unpaired restoration tasks. Based on our framework, we propose UnIRnet and UnPRnet for unpaired image and point cloud restoration tasks, respectively. It is demonstrated that our degradation representation learning scheme can extract discriminative representations to obtain accurate degradation information. Experiments on unpaired image and point cloud restoration tasks show that our UnIRnet and UnPRnet achieve state-of-the-art performance.

Comparisons of Four Acne Grading Systems Recommended in China, Korea, and Japan.

Introduction: Acne vulgaris is one of the most prevalent skin disorders. The treatment regimen depends on how severe the AV is. The acne grading system is crucial for clinical and research work. The aim of this work was to evaluate intra-grader differences between 5 investigators using Acne Grading System (AGS), the International Improved Grading System (IIGS), the Korean Acne Grading System (KAGS), and the Japanese Acne Grading System (JAGS) to assess acne vulgaris (AV) severity. Patients and Methods: AV sufferers from dermatological clinics of five hospitals in China were the subject of a prospective cross-sectional study. AGS, IIGS, KAGS and JAGS were utilized by 5 investigators to verify the severity of AV. The correlation between AGS, IIGS, KAGS and JAGS was examined. Results: A complete of 1107 AV sufferers were enrolled in the study. There were indications that the AGS, IIGS, KAGS and JAGS had sufficient internal consistency. As for the reliability amongst raters, AGS, IIGS, KAGS and JAGS confirmed gorgeous reliability. There were strong correlations amongst AGS, IIGS, KAGS and JAGS (P≤0.01). The interior reliability of investigator 1 one year ago and later after usage of AGS and IIGS was excellent (P≤0.01). Conclusion: IIGS and AGS exhibited great correlation with KAGS and JAGS and were highly reliable.

Disentangling Light Fields for Super-Resolution and Disparity Estimation.

Light field (LF) cameras record both intensity and directions of light rays, and encode 3D scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have been proposed for various LF image processing tasks. However, it is challenging for CNNs to effectively process LF images since the spatial and angular information are highly inter-twined with varying disparities. In this paper, we propose a generic mechanism to disentangle these coupled information for LF image processing. Specifically, we first design a class of domain-specific convolutions to disentangle LFs from different dimensions, and then leverage these disentangled features by designing task-specific modules. Our disentangling mechanism can well incorporate the LF structure prior and effectively handle 4D LF data. Based on the proposed mechanism, we develop three networks (i.e., DistgSSR, DistgASR and DistgDisp) for spatial super-resolution, angular super-resolution and disparity estimation. Experimental results show that our networks achieve state-of-the-art performance on all these three tasks, which demonstrates the effectiveness, efficiency, and generality of our disentangling mechanism. Project page: https://yingqianwang.github.io/DistgLF/.

Parallax Attention for Unsupervised Stereo Correspondence Learning.

Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. To exploit 3D cues within stereo images, recent CNN based methods commonly use cost volume techniques to capture stereo correspondence over large disparities. However, since disparities can vary significantly for stereo cameras with different baselines, focal lengths and resolutions, the fixed maximum disparity used in cost volume techniques hinders them to handle different stereo image pairs with large disparity variations. In this paper, we propose a generic parallax-attention mechanism (PAM) to capture stereo correspondence regardless of disparity variations. Our PAM integrates epipolar constraints with attention mechanism to calculate feature similarities along the epipolar line to capture stereo correspondence. Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks. Moreover, we introduce a new and large-scale dataset named Flickr1024 for stereo image super-resolution. Experimental results show that our PAM is generic and can effectively learn stereo correspondence under large disparity variations in an unsupervised manner. Comparative results show that our PASMnet and PASSRnet achieve the state-of-the-art performance.

Light Field Image Super-Resolution Using Deformable Convolution.

Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce beneficial angular information for image super-resolution (SR). However, it is challenging to incorporate angular information due to disparities among LF images. In this paper, we propose a deformable convolution network (i.e., LF-DFnet) to handle the disparity problem for LF image SR. Specifically, we design an angular deformable alignment module (ADAM) for feature-level alignment. Based on ADAM, we further propose a collect-and-distribute approach to perform bidirectional alignment between the center-view feature and each side-view feature. Using our approach, angular information can be well incorporated and encoded into features of each view, which benefits the SR reconstruction of all LF images. Moreover, we develop a baseline-adjustable LF dataset to evaluate SR performance under different disparity variations. Experiments on both public and our self-developed datasets have demonstrated the superiority of our method. Our LF-DFnet can generate high-resolution images with more faithful details and achieve state-of-the-art reconstruction accuracy. Besides, our LF-DFnet is more robust to disparity variations, which has not been well addressed in literature.

Innovative management programme reduces environmental impacts in Chinese vegetable production.

China produces half of the world's vegetables. The production uses 1.7% of the global harvest area of crops but accounts for 7.8% of the chemical fertilizers and 6.6% of crop-sourced greenhouse gas (GHG) emissions worldwide. Using an innovative management programme, the integrated knowledge and products strategy (IKPS), we demonstrate opportunities for producing more vegetables with lower environmental impacts in China's vegetable production systems. Combining soil-crop system management practices with enhanced-efficiency fertilizer products, IKPS was tested through 54 site-year field experiments in China's major agro-ecological zones by a national research network over 12 years. Compared with current farming practices, the adoption of IKPS decreased the nitrogen (N) application rate by 38%, N surplus by 65% and GHG emissions by 28%, while increasing yield by 17%. Scenario analyses showed that adoption of IKPS in China's vegetable production could mitigate resource and environmental burdens while enhancing food and nutrition security.

[Effects of different slow/controlled release fertilizers on yield, quality of fresh maize and ammonia emissions]. / 不同缓控释肥对鲜食玉米产量、品质及氨挥发的影响.

A field experiment with five treatments, control (CK, no fertilizer), conventional fertilization (U), double-effect inhibitor synergistic urea (DU), coated urea (CU) and slow/controlled release urea mixture (CDU), was conducted to investigate the effects of conventional fertilization (240 kg N·hm-2) and one-off application of different slow/controlled release fertilizers (180 kg N·hm-2) on the yield and quality of fresh maize, soil inorganic nitrogen (N), and ammonia (NH3) emissions. The results showed that the total amount of ammonia volatilization was the highest in treatment of conventional fertilization (U), with N topdressing being an important source of NH3 emission. Compared with U treatment, the NH3 volatilization in the DU, CU, and CDU treatments was reduced by 78%-81%. At harvest stage, the soil layer of 80-100 cm in the U treatment had the highest nitrate concentration (51.6 mg·kg-1), resulting in a high risk of N leaching. In contrast, the nitrate concentrations in the same soil layer in the slow/controlled release fertilizer treatments were much lower, reducing the risk of leaching. In comparison with U, three slow/controlled release fertilizer treatments with 25% lower N application did not decrease yield but increased seed Vc, soluble sugar and protein contents. The agronomic efficiency and economic benefit of DU treatment were the highest among three slow/controlled release fertilizers treatments. In conclusion, the application of new type of slow/controlled release fertilizer could improve the yield and quality of fresh maize, and significantly reduce the risk of ammonia loss and N leaching. Considering the higher cost of the polymer coated urea, the double-effect inhibitor urea has lower cost and is more convenient to make. It is therefore a better choice to fresh maize planting.

[Effects of applying controlled-release fertilizer blended with conventional nitrogen fertilizer on Chinese cabbage yield and quality as well as nitrogen losses].

An open field experiment was conducted to study the effects of applying controlled-release fertilizer blended with rapidly available chemical N fertilizer on Chinese cabbage yield and quality as well as nitrogen losses, including ammonia volatilization and NO3- -N accumulation and leaching in Beijing suburb. The results showed that a combined application of 2:1 controlled-release fertilizer and urea fertilizer (total N rate 150 kg x hm(-2)) did not induce the reduction of Chinese cabbage yield, and decreased the leaf nitrate and organic acid contents significantly, compared with conventional urea N application (300 kg x hm(-2)), and had no significant difference in the cabbage yield and leaf nitrate content, compared with applying 150 kg x hm(-2) of urea N. The combined application of 2:1 controlled-release fertilizer and urea fertilizer improved the N use efficiency of Chinese cabbage, and reduced the ammonia volatilization and NO3- -N leaching. At harvest, the NO3- -N concentrations in 20-40, 60-80 and 80-100 cm soil layers were significantly lower in the combined application treatment than in urea N treatment.