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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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相关实验视频

Updated: Jul 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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端到端深度引导重新照明使用基于深度学习的轻量级方法.

Sabari Nathan1, Priya Kansal1

  • 1Couger Inc., Tokyo 150-0001, Japan.

Journal of imaging
|September 27, 2023
PubMed
概括
此摘要是机器生成的。

这项研究提出了一个新的深度学习模型用于图像重新照明,使用深度信息来调整照明,同时保持内容完整. 该模型有效地处理各种照明条件,产生现实的结果.

关键词:
深度指导的深度指导.图像增强 图像增强 图像增强图像重新照明的图像重新照明

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科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 图像重新照明对于改变照明条件,同时保持视觉内容至关重要.
  • 现有的方法可能会在复杂的照明变化中扎.

研究的目的:

  • 为深度引导图像重新照明引入双模轻量级深度学习模型.
  • 为了增强特征表示,以提高重新点亮的准确性.

主要方法:

  • 使用Res2Net挤压块来捕获远程依赖关系并增强特征表示.
  • 采用了一个用Res2Net Squeezed块编码解码器架构.
  • 在VIDIT数据集 (300个图像三重组) 上接受培训和评估.

主要成果:

  • 该模型有效地处理复杂的照明变化,包括不同的照明角度和颜色温度.
  • 实现了高亮度准确性,通过PSNR,SSIM和视觉质量指标进行验证.
  • 证明了改进的信息流,用于实现现实的再照明图像生成.

结论:

  • 拟议的深度引导重新照明模型是有效和高效的.
  • 在Res2Net挤压块显著有助于处理复杂的照明.
  • 这种方法可以生成具有高保真度的真实再照明图像.