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Retinex-Based Fast Algorithm for Low-Light Image Enhancement.

Shouxin Liu1, Wei Long1, Lei He1

  • 1School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.

Entropy (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

We developed a fast Retinex-based algorithm (RBFA) for low-light image enhancement. This method effectively restores details in dark images, offering improved quality and efficiency over existing techniques.

Keywords:
HSV color spaceRetinexgamma correctionimage enhancementlow-light image

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Low-light conditions severely degrade image quality, obscuring details and reducing contrast.
  • Existing image enhancement algorithms often struggle with preserving details and color fidelity.

Purpose of the Study:

  • To introduce a novel Retinex-based fast algorithm (RBFA) for effective low-light image enhancement.
  • To restore information lost due to low illuminance and improve overall image clarity.

Main Methods:

  • Conversion of RGB images to HSV color space, followed by dynamic range stretching of the V component.
  • Estimation of illumination using adaptive gamma correction and application of the Retinex model for brightness enhancement.
  • Gray level dynamic range stretching for contrast improvement and a dedicated mapping function for color saturation correction.

Main Results:

  • The RBFA successfully enhances low-light images, restoring details and improving contrast.
  • Qualitative and quantitative evaluations demonstrate superior performance compared to state-of-the-art methods.
  • The proposed algorithm exhibits lower computational complexity, enabling faster processing.

Conclusions:

  • The Retinex-based fast algorithm (RBFA) provides a robust solution for low-light image enhancement.
  • RBFA achieves superior image quality and efficiency, making it suitable for practical applications.
  • The method effectively addresses challenges related to illumination, contrast, and color saturation in dark images.