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Related Concept Videos

Transformers01:26

Transformers

1.2K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.2K
Types Of Transformers01:16

Types Of Transformers

1.1K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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The Ideal Transformer01:26

The Ideal Transformer

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
915
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

213
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
213
Transformation01:26

Transformation

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Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
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Transformers in Distribution System01:27

Transformers in Distribution System

165
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Image colorization based on transformer.

Min Xu1,2

  • 1School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China. minxu@sspu.edu.cn.

Scientific Reports
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a transformer-based deep learning method for automatic grayscale image colorization. The approach achieves natural coloring and rich details, significantly improving black and white image enhancement.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Grayscale images lack visual information.
  • Manual colorization is time-consuming and subjective.
  • Automated methods are needed for realistic image enhancement.

Purpose of the Study:

  • To develop a transformer-based deep learning model for automatic grayscale image colorization.
  • To improve the accuracy and visual quality of colorized images.
  • To leverage deep architectures and attention mechanisms for feature extraction and prediction.

Main Methods:

  • Utilized a transformer-based deep architecture with stacked encoder-decoder layers.
  • Employed a self-attention mechanism and contextual information for predicting chrominance components (e.g., ab channels in CIELAB).
  • Integrated the input luminance channel (L channel) with predicted chrominance channels for final colorization.

Main Results:

  • Achieved significant performance improvements in black and white image colorization.
  • Generated images exhibit natural coloring and rich details.
  • Demonstrated superior pixel accuracy and visual quality compared to existing methods.

Conclusions:

  • The proposed transformer-based method effectively colorizes grayscale images.
  • The model's deep architecture and attention mechanisms enhance feature extraction and prediction.
  • The results show high value for practical applications in image enhancement and restoration.