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Large-Small-Scale Structure Blended U-Net for Brightening Low-Light Images.

Hao Cheng1, Kaixin Pan1, Haoxiang Lu1

  • 1School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.

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Summary
This summary is machine-generated.

This study introduces a novel dual-branch network for low-light image enhancement. The proposed method effectively brightens images while improving color correction and detail restoration, outperforming existing techniques.

Keywords:
U-Netcolor correctionimage enhancementlarge–small-scale structure

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Existing low-light image enhancement methods often struggle with detail restoration and accurate color correction.
  • Improving visual quality in low-illumination conditions remains a significant challenge in image processing.

Purpose of the Study:

  • To develop a novel dual-branch network for superior low-light image enhancement.
  • To address limitations in detail enhancement and color correction present in current methods.

Main Methods:

  • A dual-branch network comprising a color correction network (CC-Net) and a light-boosting network (LB-Net).
  • Utilizing the CIELAB color space for luminosity and color component extraction.
  • Implementing a U-shaped network for CC-Net and a large-small-scale structure for LB-Net to explore multiscale features.
  • Incorporating an efficient feature interaction module for cross-branch information exchange.

Main Results:

  • The proposed method significantly enhances image brightness, detail, and color fidelity in low-illumination scenarios.
  • Experimental results on public benchmarks show superior performance compared to state-of-the-art low-light enhancement techniques.
  • Demonstrated improvement in object detection performance under low-light conditions.

Conclusions:

  • The dual-branch network effectively tackles the challenges of detail enhancement and color correction in low-light images.
  • The proposed approach offers a robust solution for improving the quality of low-light imagery and its applications.