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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Precipitation Gravimetry01:03

Precipitation Gravimetry

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
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Types of Coprecipitation01:10

Types of Coprecipitation

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Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
Sometimes, ions in a crystal lattice can undergo isomorphous replacement by inclusions of similar charge and size. For...
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Boundary Layer Characteristics01:18

Boundary Layer Characteristics

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When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
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Precipitation of Ions03:11

Precipitation of Ions

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Predicting Precipitation
The equation that describes the equilibrium between solid calcium carbonate and its solvated ions is:
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Scattering And Absorption of Light in Planetary Regoliths
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A Joint De-Rain and De-Mist Network Based on the Atmospheric Scattering Model.

Linyun Gu1, Huahu Xu2, Xiaojin Ma3

  • 1School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China.

Journal of Imaging
|July 28, 2023
PubMed
Summary

This study introduces a new method to remove rain streaks and mist halos from images, significantly improving clarity. The Joint De-rain and De-mist Network (JDDN) enhances image quality in adverse weather conditions.

Keywords:
atmospheric scattering modelimages de-rainmulti-scale convolution

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Rain and mist cause visual distortions like streaks and halos, degrading image quality.
  • Existing methods struggle to effectively remove both rain and mist simultaneously.

Purpose of the Study:

  • To develop a novel approach for simultaneous de-raining and de-misting of images.
  • To enhance the clarity and quality of images captured in rainy and misty conditions.

Main Methods:

  • A rain and mist model based on atmospheric scattering for initial image reconstruction.
  • Deep Memory Block (DMB) for selective extraction of rain and mist layer transfer spectra.
  • Multi-scale Convolution Block (MCB) for feature extraction and model enhancement.

Main Results:

  • The proposed Joint De-rain and De-mist Network (JDDN) effectively removes both streaks and halos.
  • JDDN outperforms state-of-the-art deep learning methods on synthetic and real-world datasets.
  • An average improvement of 0.29 dB was achieved on heavy-rainy-image datasets.

Conclusions:

  • The JDDN model offers a robust and accurate solution for joint de-raining and de-misting.
  • This approach significantly improves image quality in adverse weather.
  • The method demonstrates superior performance compared to existing techniques.