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相关概念视频

Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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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 in...
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Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

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Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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相关实验视频

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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DCD-Net:用于低光图像增强的脱中心分解网络.

Wei Wang1, Yi Zhu1, Mingming Zhang1

  • 1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
概括

本研究介绍了脱中心分解网络 (DCD-Net) 用于低光图像增强. DCD-Net通过改进照明-反射分离来改善基于Retinex的分解,优于现有方法.

科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 人工智能的人工智能

背景情况:

  • 低光图像增强对于各种应用至关重要.
  • 现有的方法往往忽视了分解网络在分离照明和反射率方面的关键作用.
  • 当前的分解网络可能不严格遵守Retinex理论.

研究的目的:

  • 提出一种新的脱中心分解网络 (DCD-Net),用于低光下图像增强.
  • 解决现有方法的局限性,重点关注照明-反射脱的改进.
  • 在不依赖单独的增强网络的情况下,提高Retinex分解的质量.

主要方法:

  • DCD-Net包括一个预处理网络,使用自主监督学习来删除 Retinex 不兼容的功能.
  • 分解网络采用双门定向反射模块 (DGD-RM) 和反射引导多头自动注意 (RG-MSA) 进行反射恢复.
  • 照明估计使用离散等边变换 (DCT) 来进行局部-全球估计.

主要成果:

  • 在LOL v1上获得了20.87/0.770的PSNR/SSIM分数,在MIT数据集上获得了21.66/0.864.
  • 在五个未配对的数据集中获得了平均NIQE得分3.420.
  • 通过定性和定量分析,与最先进的方法相比,表现出优越的性能.
关键词:
离散的等号变换 (DCT)变压器变压器变压器卷积神经网络是一种卷积神经网络.分解网络的分解网络.照明和反射率的变化在低光下增强图像增强.

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结论:

  • 通过优先考虑强大的照明-反射脱,DCD-Net有效地增强了低光图像.
  • 拟议的模块 (DGD-RM,RG-MSA,基于DCT的照明估计) 对网络的性能作出了重大贡献.
  • 废弃研究证实了DCD-Net架构中的单个组件的有效性.