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A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model.

Shiping Ma1, Hongqiang Ma2, Yuelei Xu3,4

  • 1Aeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China. mashiping@126.com.

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Summary
This summary is machine-generated.

This study introduces a new algorithm to enhance low-light sensor images using the HSI color model. The method significantly improves image brightness and contrast without causing color distortion.

Keywords:
Retinex modelbatch normalizationcolor modelconvolutional neural networkfeature learningimage enhancement

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Low illumination conditions degrade sensor images, resulting in low visibility, brightness, and contrast.
  • Existing image enhancement methods may suffer from limitations such as color distortion or over-enhancement.

Purpose of the Study:

  • To propose a novel low-light sensor image enhancement algorithm.
  • To improve image quality by enhancing brightness and contrast while preserving color fidelity.

Main Methods:

  • A dataset generation method based on the Retinex model was developed to address data scarcity.
  • Images were converted from RGB to the HSI color space.
  • The saturation (S) component was processed using a segmentation exponential method.
  • A Deep Convolutional Neural Network was designed to enhance the intensity (I) component.

Main Results:

  • The proposed algorithm significantly enhances image brightness and contrast.
  • The method effectively avoids color distortion and over-enhancement compared to state-of-the-art approaches.
  • Experimental results demonstrate a notable improvement in sensor image quality.

Conclusions:

  • The developed algorithm offers an effective solution for enhancing low-light sensor images.
  • The HSI color model-based approach successfully improves image visibility and detail preservation.
  • This research contributes to better image quality in challenging environmental conditions.