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Updated: Apr 12, 2026
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Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
Published on: July 11, 2025
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Integrating perceptual cues with mixture-of-experts for low-light image restoration
Yuezhou Li1, Yuzhen Niu2, Huangbiao Xu2
1Digital Fujian Research Institute of Big Data for Agriculture and Forestry, College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China; College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China.
Summary
This study introduces an innovative method for enhancing low-light images by integrating perceptual cues with a mixture-of-experts (IPCMoE) model. The approach effectively addresses image degradations, improving visual quality in challenging nighttime scenes.
Area of Science:
- Computer Vision
- Image Processing
- Artificial Intelligence
Background:
- Nighttime image perception is hindered by low-light, noise, motion blur, and low resolution.
- Existing methods struggle with diverse degradation patterns and intensities, causing artifacts and inconsistent illumination.
Purpose of the Study:
- To develop a flexible image processing method for low-light, low-quality images.
- To address limitations in current joint degradation correction techniques.
Main Methods:
- Proposed an Image Processing with Mixture-of-Experts (IPCMoE) model integrating perceptual cues.
- Designed customized routers and task-dependent experts, including a texture memorial MoE and an enhancement MoE.
- Employed a selective collaboration approach for feature enhancement and texture restoration.
Main Results:
- IPCMoE demonstrated superior performance compared to state-of-the-art models on various benchmarks.
- Achieved effective handling of complex low-light scenes with improved feature enlightening and texture restoration.
- Successfully balanced feature enlightening and texture restoration for dynamic image processing.
Conclusions:
- The proposed IPCMoE model offers a robust solution for enhancing degraded nighttime images.
- Integrating perceptual cues and a mixture-of-experts approach leads to significant improvements in image quality.
- This method provides a flexible and adaptive framework for low-light image enhancement.
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