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Related Experiment Videos

Lightweight Low-Light Enhancement Network with Multi-Bio-Inspired Visual Mechanisms.

Yafeng Zhao1, Xiang Li1, Shuaipeng Hao1

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

Biomimetics (Basel, Switzerland)
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a bio-inspired model for low-light image enhancement, balancing complexity and quality for edge devices. It achieves superior perceptual quality with minimal parameters, outperforming existing lightweight methods.

Keywords:
bioinspired visual perceptionlightweight neural networkslow-light image enhancementperceptual quality optimizationretinal adaptation mechanism

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Bio-inspired Computing
  • Image Processing

Background:

  • Edge deployment scenarios necessitate lightweight models for low-light image enhancement.
  • Existing lightweight models often struggle with a trade-off between model complexity and perceptual quality.
  • Resource constraints in edge devices limit the application of complex image enhancement algorithms.

Purpose of the Study:

  • To propose a novel perceptual quality optimization model for low-light image enhancement inspired by biological visual mechanisms.
  • To address the limitations of existing lightweight models in resource-constrained edge environments.
  • To improve both objective metrics (PSNR, SSIM) and subjective visual quality (LPIPS).

Main Methods:

  • Introduced a GT-Mean loss to simulate mammalian retinal luminance adaptation, mitigating exposure inconsistency.
  • Incorporated LPIPS loss to align with human visual system (HVS) preferences for enhanced subjective quality.
  • Integrated biologically inspired modules: texture-aware attention, multi-scale feature fusion, and chrominance denoising.

Main Results:

  • The proposed model achieves state-of-the-art performance on the LOL dataset series.
  • Maintained an extremely low parameter count (0.52 M), demonstrating high efficiency.
  • Outperformed existing lightweight methods in PSNR, SSIM, and LPIPS metrics.

Conclusions:

  • The bio-inspired model offers an efficient solution for perceptual quality optimization in low-light image enhancement.
  • The model is suitable for resource-constrained edge deployment scenarios.
  • This work advances bio-inspired visual sensing with practical applications in edge computing.