Publications by authors named "Jungang Yang"

Article Synopsis
  • Restoration tasks in low-level vision focus on recovering high-quality (HQ) data from low-quality (LQ) inputs, with increasing interest in unpaired methods that don't require matched datasets.
  • Diverse and unknown degradation types in real-world scenarios present challenges for these unpaired learning methods.
  • The paper introduces a degradation representation learning scheme and a framework featuring degradation-aware convolutions, resulting in two models, UnIRnet and UnPRnet, which demonstrate state-of-the-art performance in restoring images and point clouds respectively.
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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.

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Article Synopsis
  • Recent advancements in deep neural networks (DNNs) have improved light field (LF) image super-resolution (SR), but most current methods rely on a single fixed degradation process, limiting their effectiveness on real LF images with varied degradation.
  • The proposed method develops a practical LF degradation model to better represent how real LF images degrade, and incorporates this degradation into a convolutional neural network to enhance the SR process.
  • Extensive tests show that this approach outperforms existing methods in super-resolving both synthetically and naturally degraded LF images, demonstrating improved adaptability and performance.
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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 strains (KC01, KC02, and KC03) were isolated from soft-rotted Chinese cabbage in Beijing, China.

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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.

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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.

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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.

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China produces half of the world's vegetables. The production uses 1.7% of the global harvest area of crops but accounts for 7.

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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.

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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.

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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) and one-off application of different slow/controlled release fertilizers (180 kg N·hm) on the yield and quality of fresh maize, soil inorganic nitrogen (N), and ammonia (NH) 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 NH emission. Compared with U treatment, the NH volatilization in the DU, CU, and CDU treatments was reduced by 78%-81%.

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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.

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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.

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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.

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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.

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